the fisher neyman pearson theories of testing hypotheses one theory or two abstract the fisher and neyman pearson approaches to testing statistical hypotheses are compared with respect to their attitudes to the interpretation of the outcome to power to conditioning and to the use of fixed significance levels it is argued that despite basic philosophical differences in their main practical aspects the two theories are complementary rather than contradictory and that a unified approach is possible that combines the best features of both as applications the controversies about the behrens fisher problem and the comparison of two binomials 2 2 tables are considered from the present point of view

regression analysis for recurrent events data under dependent censoring summary recurrent events data are commonly seen in longitudinal follow up studies dependent censoring often occurs due to death or exclusion from the study related to the disease process in this article we assume flexible marginal regression models on the recurrence process and the dependent censoring time without specifying their dependence structure the proposed model generalizes the approach by ghosh and lin 2003 biometrics 59 877 885 the technique of artificial censoring provides a way to maintain the homogeneity of the hypothetical error variables under dependent censoring here we propose to apply this technique to two gehan type statistics one considers only order information for pairs whereas the other utilizes additional information of observed censoring times available for recurrence data a model checking procedure is also proposed to assess the adequacy of the fitted model the proposed estimators have good asymptotic properties their finite sample performances are examined via simulations finally the proposed methods are applied to analyze the aids linked to the intravenous experiences cohort data

disturbance attenuation for constrained discrete time systems via receding horizon controls in this note we propose new receding horizon h sub spl infin control rhhc schemes for linear input constrained discrete time invariant systems with disturbances the proposed control schemes are based on the dynamic game problem of a finite horizon cost function with a fixed finite terminal weighting matrix and a one horizon cost function with time varying finite terminal weighting matrices respectively we show that the resulting rhhcs guarantee closed loop stability in the absence of disturbances and h sub spl infin norm bound for 2 norm bounded disturbances we also show that the proposed schemes can easily be implemented via linear matrix inequality optimization we illustrate the effectiveness of the proposed schemes through simulations

kernel machine approach to testing the significance of multiple genetic markers for risk prediction summary there is growing evidence that genomic and proteomic research holds great potentials for changing irrevocably the practice of medicine the ability to identify important genomic and biological markers for risk assessment can have a great impact in public health from disease prevention to detection to treatment selection however the potentially large number of markers and the complexity in the relationship between the markers and the outcome of interest impose a grand challenge in developing accurate risk prediction models the standard approach to identifying important markers often assesses the marginal effects of individual markers on a phenotype of interest when multiple markers relate to the phenotype simultaneously via a complex structure such a type of marginal analysis may not be effective to overcome such difficulties we employ a kernel machine cox regression f ramework and propose an efficient score test to assess the overall effect of a set of markers such as genes within a pathway or a network on survival outcomes the proposed test has the advantage of capturing the potentially non linear effects without explicitly specifying a particular non linear functional form to approximate the null distribution of the score statistic we propose a simple resampling procedure that can be easily implemented in practice numerical studies suggest that the test performs well with respect to both empirical size and power even when the number of variables in a gene set is not small compared to the sample size

rank based inference for the accelerated failure time model summary a broad class of rank based monotone estimating functions is developed for the semiparametric accelerated failure time model with censored observations the corresponding estimators can be obtained via linear programming and are shown to be consistent and asymptotically normal the limiting covariance matrices can be estimated by a resampling technique which does not involve nonparametric density estimation or numerical derivatives the new estimators represent consistent roots of the non monotone estimating equations based on the familiar weighted log rank statistics simulation studies demonstrate that the proposed methods perform well in practical settings two real examples are provided

model checking for roc regression analysis the receiver operating characteristic roc curve is a prominent tool for characterizing the accuracy of a continuous diagnostic test to account for factors that might influence the test accuracy various roc regression methods have been proposed however as in any regression analysis when the assumed models do not fit the data well these methods may render invalid and misleading results to date practical model checking techniques suitable for validating existing roc regression models are not yet available in this article we develop cumulative residual based procedures to graphically and numerically assess the goodness of fit for some commonly used roc regression models and show how specific components of these models can be examined within this framework we derive asymptotic null distributions for the residual processes and discuss resampling procedures to approximate these distributions in practice we illustrate our methods with a dataset from the cystic fibrosis registry

a simple resampling method by perturbing the minimand suppose that under a semiparametric setting an estimator of a vector of parameters of interest is obtained by optimising an objective function which has a u process structure the covariance matrix of the estimator is generally a function of the underlying density function which may be difficult to estimate well by conventional methods in this paper we present a simple resampling method by perturbing the objective function repeatedly inferences of the parameters can then be made based on a large collection of the resulting optimisers we illustrate our proposal by three examples with a heteroscedastic regression model

simple resampling methods for censored regression quantiles abstract powell journal of econometrics 25 1984 303 325 journal of econometrics 32 1986 143 155 considered censored regression quantile estimators the asymptotic covariance matrices of his estimators depend on the error densities and are therefore difficult to estimate reliably the difficulty may be avoided by applying the bootstrap method hahn econometric theory 11 1995 105 121 calculation of the estimators however requires solving a nonsmooth and nonconvex minimization problem resulting in high computational costs in implementing the bootstrap we propose in this paper computationally simple resampling methods by convexfying powell s approach in the resampling stage a major advantage of the new methods is that they can be implemented by efficient linear programming simulation studies show that the methods are reliable even with moderate sample sizes

characterization of the solution to a constrained h optimal control problem this paper obtains an explicit solution to a finite horizon min max optimal control problem where the system is linear and discrete time with control and state constraints and the cost quadratic the disturbance is negatively costed as in the standard h problem and is constrained the cost is minimized over control policies and maximized over disturbance sequences so that the solution yields a feedback control it is shown that under certain conditions the value function is piecewise quadratic and the optimal control policy piecewise affine being quadratic and affine respectively in polyhedra that partition the domain of the value function

stabilizing receding horizon h sub spl infin controls for linear continuous time varying systems in this note new matrix inequality conditions on the terminal weighting matrices are proposed for linear continuous time varying systems under these conditions nonincreasing and nondecreasing monotonicities of the saddle point value of a dynamic game are shown to be guaranteed it is proved that the proposed terminal inequality conditions ensure the closed loop stability of the receding horizon h sub spl infin control rhhc the stabilizing rhhc guarantees the h sub spl infin norm bound of the closed loop system the proposed terminal inequality conditions for the monotonicity of the saddle point value and the closed loop stability include most well known existing terminal conditions as special cases the results for time invariant systems are obtained correspondingly from those in the time varying case

a smooth block bootstrap for quantile regression with time series quantile regression allows for broad conditional characterizations of a response distribution beyond conditional means and is of increasing interest in economic and financial applications because quantile regression estimators have complex limiting distributions several bootstrap methods for the independent data setting have been proposed many of which involve smoothing steps to improve bootstrap approximations currently no similar advances in smoothed bootstraps exist for quantile regression with dependent data to this end we establish a smooth tapered block bootstrap procedure for approximating the distribution of quantile regression estimators for time series this bootstrap involves two rounds of smoothing in resampling individual observations are resampled via kernel smoothing techniques and resampled data blocks are smoothed by tapering the smooth bootstrap results in performance improvements over previous unsmoothed versions of the block bootstrap as well as normal approximations based on powell s kernel variance estimator which are common in application our theoretical results correct errors in proofs for earlier and simpler versions of the unsmoothed moving blocks bootstrap for quantile regression and broaden the validity of block bootstraps for this problem under weak conditions we illustrate the smooth bootstrap through numerical studies and examples

smooth bootstrap methods for analysis of longitudinal data in analysis of longitudinal data the variance matrix of the parameter estimates is usually estimated by the sandwich method in which the variance for each subject is estimated by its residual products we propose smooth bootstrap methods by perturbing the estimating functions to obtain bootstrapped realizations of the parameter estimates for statistical inference our extensive simulation studies indicate that the variance estimators by our proposed methods can not only correct the bias of the sandwich estimator but also improve the confidence interval coverage we applied the proposed method to a data set from a clinical trial of antibiotics for leprosy

estimating a treatment effect with repeated measurements accounting for varying effectiveness duration to assess treatment efficacy in clinical trials certain clinical outcomes are repeatedly measured over time for the same subject the difference in their means may characterize a treatment effect since treatment effectiveness lag and saturation times may exist erosion of treatment effect often occurs during the observation period instead of using models based on ad hoc parametric or purely nonparametric time varying coefficients we model the treatment effectiveness durations which are the time intervals between the lag and saturation times then we use some mean response models to include such treatment effectiveness durations our methodology is demonstrated by simulations and analysis of a landmark hiv aids clinical trial of short course nevirapine against mother to child hiv vertical transmission during labour and delivery

nonparametric estimation and inference on conditional quantile processes this paper presents estimation methods and asymptotic theory for the analysis of a nonparametrically specified conditional quantile process two estimators based on local linear regressions are proposed the first estimator applies simple inequality constraints while the second uses rearrangement to maintain quantile monotonicity the bandwidth parameter is allowed to vary across quantiles to adapt to data sparsity for inference the paper first establishes a uniform bahadur representation and then shows that the two estimators converge weakly to the same limiting gaussian process as an empirical illustration the paper considers a dataset from project star and delivers two new findings

inference on counterfactual distributions counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics in this article we develop modeling and inference tools for counterfactual distributions based on regression methods the counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates for either of these scenarios we derive joint functional central limit theorems and bootstrap validity results for regression based estimators of the status quo and counterfactual outcome distributions these results allow us to construct simultaneous confidence sets for function valued effects of the counterfactual changes including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals these confidence sets can be used to test functional hypotheses such as no effect positive effect or stochastic dominance our theory applies to general counterfactual changes and covers the main regression methods including classical quantile duration and distribution regressions we illustrate the results with an empirical application to wage decompositions using data for the united states as a part of developing the main results we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the entire conditional distribution we show that distribution regression encompasses the cox duration regression and represents a useful alternative to quantile regression we establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals publication abstract

a two sample comparison for multiple ordered event data summary a longitudinal study is conducted to compare the process of a particular disease between two groups the process of the disease is monitored according to which of several ordered events occur in the paper the sojourn time between two successive events is considered as the outcome of interest the group effects on the sojourn times of the multiple events are parameterized by scale changes in a semiparametric accelerated failure time model where the dependence structure among the multivariate sojourn times is unspecified suppose that the sojourn times are subject to dependent censoring and the censoring times are observed for all subjects a log rank type estimating approach by rescaling the sojourn times and the dependent censoring times into the same distribution is constructed to estimate the group effects and the corresponding estimators are consistent and asymptotically normal without the dependent censoring the independent censoring times in general are not available for the uncensored data in order to complete the censoring information pseudo censoring times are generated from the corresponding nonparametrically estimated survival function in each group and we can still obtained unbiased estimating functions for the group effects a real application and a simulation study are conducted to illustrate the proposed methods

a monte carlo approach for change point detection in the cox proportional hazards model detecting a time lag of treatment effect or identifying change points in a hazard function is of great interest and importance in survival analysis the testing procedures hereto are primarily based on analytical approximations for the asymptotic null distribution of either the likelihood ratio test or the score test in the presence of random censoring and or covariates however the justification for the limiting distribution often requires some technical assumptions and conditions that are difficult to verify in practice moreover a satisfactory asymptotic theory for testing the existence of multiple change points in hazard function has not emerged in this paper we consider maximal score tests for detecting change point s in the cox proportional hazards model with censored data we propose to use a simple monte carlo approach for assessing the statistical significance of tests the proposed approach is applicable for testing a single change point in the cox model with covariates and sample stratifications over various types of candidate regions including discrete time point sets or disjoint intervals we also show that the proposed test statistics and the monte carlo procedure are well applicable under situations with multiple change points simulation studies and an analysis of a real data from a randomized cancer trial are conducted to demonstrate the finite sample performance of the proposed approach

simple and fast overidentified rank estimation for right censored length biased data and backward recurrence time summary length biased survival data subject to right censoring are often collected from a prevalent cohort however informative right censoring induced by the sampling design creates challenges in methodological development while certain conditioning arguments could circumvent the problem of informative censoring related rank estimation methods are typically inefficient because the marginal likelihood of the backward recurrence time is not ancillary under a semiparametric accelerated failure time model an overidentified set of log rank estimating equations is constructed based on the left truncated right censored data and backward recurrence time efficient combination of the estimating equations is simplified by exploiting an asymptotic independence property between two sets of estimating equations a fast algorithm is studied for solving non smooth non monotone estimating equations simulation studies confirm that the overidentified rank estimator can have a substantially improved estimation efficiency compared to just identified rank estimators the proposed method is applied to a dementia study for illustration

control of a 3d piezo actuating table by using an adaptive sliding mode controller for a drilling process recently the micropositioner has become an important developing target for achieving the requirements of precision machinery the piezo actuating device plays a very important role in this application area in this paper a model free adaptive sliding mode controller is proposed for a 3d piezo actuating system because of the system s hysteresis nonlinearity and time varying characteristics this control strategy employs the functional approximation technique to establish the unknown function for releasing the model based requirements of the sliding mode control the update laws for the coefficients of the fourier series function parameters are derived from a lyapunov function to guarantee the control system stability to verify the effectiveness of the proposed controller drilling process control using the designed controller is investigated in this paper

markov chain marginal bootstrap markov chain marginal bootstrap mcmb is a new method for constructing confidence intervals or regions for maximum likelihood estimators of certain parametric models and for a wide class of m estimators of linear regression the mcmb method distinguishes itself from the usual bootstrap methods in two important aspects it involves solving only one dimensional equations for parameters of any dimension and produces a markov chain rather than a conditionally independent sequence it is designed to alleviate computational burdens often associated with bootstrap in high dimensional problems the validity of mcmb is established through asymptotic analyses and illustrated with empirical and simulation studies for linear regression and generalized linear models

finite sample inference for quantile regression models abstract under minimal assumptions finite sample confidence bands for quantile regression models can be constructed these confidence bands are based on the conditional pivotal property of estimating equations that quantile regression methods solve and provide valid finite sample inference for linear and nonlinear quantile models with endogenous or exogenous covariates the confidence regions can be computed using markov chain monte carlo mcmc methods we illustrate the finite sample procedure through two empirical examples estimating a heterogeneous demand elasticity and estimating heterogeneous returns to schooling we find pronounced differences between asymptotic and finite sample confidence regions in cases where the usual asymptotics are suspect

a resampling method based on pivotal estimating functions summary suppose that under a semiparametric model setting one is interested in drawing inferences about a finite dimensi onal parameter vector based on an estimating function generally a consistent point estimator j for 0 the true value for j can be easily obtained by finding a root of the corresponding estimating equation to estimate the variance of ft however may involve complicated and subjective nonparametric functional estimates in this paper a general and simple resampling method for inferences about js0 based on pivotal estimating functions is proposed the new procedure is illustrated with the quantile and rank regression models for both cases our proposal can be easily and efficiently implemented with existing statistical software

accelerated failure time models for counting processes summary we present a natural extension of the conventional accelerated failure time model for survival data to formulate the effects of covariates on the mean function of the counting process for recurrent events a class of consistent and asymptotically normal rank estimators is developed for estimating the regression parameters of the proposed model in addition a nelson aalen type estimator for the mean function of the counting process is constructed which is consistent and properly normalised converges weakly to a zeromean gaussian process we assess the finite sample properties of the proposed estimators and the associated inference procedures through monte carlo simulation and provide an application to a well known bladder cancer study

intervalwise receding horizon h sub spl infin tracking control for discrete linear periodic systems in this paper a fixed horizon h sub spl infin tracking control htc for discrete time varying systems is obtained in a state feedback form from the htc an intervalwise receding horizon htc ihtc is suggested for discrete periodic systems conditions are proposed under which ihtc can stabilize a closed loop system it is shown that under these conditions ihtc guarantees an h sub spl infin norm bound and that ihtc with integral action provides zero offset for a constant command signal the performance of ihtc is compared with those of existing controls via an example the results in this paper are also applicable to time invariant systems which belong to the class of periodic systems

estimation of covariate specific time dependent roc curves in the presence of missing biomarkers summary covariate specific time dependent roc curves are often used to evaluate the diagnostic accuracy of a biomarker with time to event outcomes when certain covariates have an impact on the test accuracy in many medical studies measurements of biomarkers are subject to missingness due to high cost or limitation of technology this article considers estimation of covariate specific time dependent roc curves in the presence of missing biomarkers to incorporate the covariate effect we assume a proportional hazards model for the failure time given the biomarker and the covariates and a semiparametric location model for the biomarker given the covariates in the presence of missing biomarkers we propose a simple weighted estimator for the roc curves where the weights are inversely proportional to the selection probability we also propose an augmented weighted estimator which utilizes information from the subjects with missing biomarkers the augmented weighted estimator enjoys the double robustness property in the sense that the estimator remains consistent if either the missing data process or the conditional distribution of the missing data given the observed data is correctly specified we derive the large sample properties of the proposed estimators and evaluate their finite sample performance using numerical studies the proposed approaches are illustrated using the us alzheimer s disease neuroimaging initiative adni dataset

sampling based approaches to calculating marginal densities abstract stochastic substitution the gibbs sampler and the sampling importance resampling algorithm can be viewed as three alternative sampling or monte carlo based approaches to the calculation of numerical estimates of marginal probability distributions the three approaches will be reviewed compared and contrasted in relation to various joint probability structures frequently encountered in applications in particular the relevance of the approaches to calculating bayesian posterior densities for a variety of structured models will be discussed and illustrated

bayes and likelihood calculations from confidence intervals recently there has been considerable progress on setting good approximate confidence intervals for a single parameter in a multi parameter family here we use these frequentist results as a convenient device for making bayes empirical bayes and likelihood inferences about a simple formula is given that produces an approximate likelihood function l x for with all nuisance parameters eliminated based on any system of approximate confidence intervals the statistician can then modify l x with bayes or empirical bayes information for without worrying about nuisance parameters

joint regression analysis of survival and quality adjusted survival summary in this paper a semiparametric bivariate linear regression model for survival and quality adjusted survival is investigated even with a parametric specification for the joint distribution maximum likelihood is not applicable because of induced informative censoring we propose inference procedures based on estimating functions the estimators are consistent and asymptotically normal hypothesis tests and confidence intervals may be constructed with easy to implement resampling techniques simultaneous regression modeling of survival and quality adjusted survival has not been studied formally our methodology gives parameter estimates that are highly interpretable in the context of a cost effectiveness analysis the usefulness of the proposal is illustrated with a breast cancer dataset

design and implementation of fuzzy sliding mode controllers for generalized projective synchronization of chaos horizontal platform systems this paper presents robust chattering free fuzzy sliding mode control fsmc as a way to achieve generalized projective synchronization gps of two horizontal chaos platform vibration systems with system uncertainty and external disturbance an appropriate sliding surface is acquired through an smc design such that the error state trajectories of a master slave system will reach such a sliding surface and approaches to the origin based on the lyapunov stability theory we address the design schemes of integration fuzzy sliding mode control where the reaching law is proposed by a set of linguistic rules and the control input is chattering free as a consequence the system is simulated and experimented to demonstrate that the error state tends to be stabilized and the chattering problem can be thus resolved implying that the slave horizontal platform system hps is gps with the master hps

variance component tests of multivariate mediation effects under composite null hypotheses mediation effects of multiple mediators are determined by two associations one between an exposure and mediators s m and the other between the mediators and an outcome conditional on the exposure m y the test for mediation effects is conducted under a composite null hypothesis that is either one of the s m and m y associations is zero or both are zeros without accounting for the composite null the type 1 error rate within a study containing a large number of multimediator tests may be much less than the expected we propose a novel test to address the issue for each mediation test j j 1 j we examine the s m and m y associations using two separate variance component tests assuming a zero mean working distribution with a common variance for the element wise s m and m y associations score tests for the variance components are constructed we transform the test statistics into two normally distributed statistics under the null using a recently developed result we conduct j hypothesis tests accounting for the composite null hypothesis by adjusting for the variances of the normally distributed statistics for the s m and m y associations advantages of the proposed test over other methods are illustrated in simulation studies and a data application where we analyze lung cancer data from the cancer genome atlas to investigate the smoking effect on gene expression through dna methylation in 15 114 genes

confidence distributions and a unifying framework for meta analysis this article develops a unifying framework as well as robust meta analysis approaches for combining studies from independent sources the device used in this combination is a confidence distribution cd which uses a distribution function instead of a point point estimator or an interval confidence interval to estimate a parameter of interest a cd function contains a wealth of information for inferences and it is a useful device for combining studies from different sources the proposed combining framework not only unifies most existing meta analysis approaches but also leads to development of new approaches we illustrate in this article that this combining framework can include both the classical methods of combining p values and modern model based meta analysis approaches we also develop under the unifying framework two new robust meta analysis approaches with supporting asymptotic theory in one approach each study size goes to infinity and in the other approach the number of studies

analysis of incomplete repeated measurements with dependent censoring times summary a frequently encountered complication in repeated measurements data analysis is that a substantial number of subjects drop out of the study in this paper several conceptually simple methods are proposed for analysing incomplete repeated measures when the measurement times vary substantially from subject to subject and the subject s drop out time may depend on the observed and unobserved outcome variables numerical studies are conducted to examine the adequacy of our procedures under practical settings a detailed illustration with an aids clinical trial is also provided

implementation of estimating function based inference procedures with markov chain monte carlo samplers under a semiparametric or nonparametric setting inferences about the unknown parameter are often made based on a nonsmooth estimating function resampling methods are quite handy for obtaining good approximations to the distribution of the consistent estimator when the estimating equation and its resampled counterparts are not difficult to solve numerically in this article we propose a simple flexible procedure that provides such approximations through the standard markov chain monte carlo sampler without solving any equations more generally the procedure may locate all possible roots of the estimating equation and provides an approximation to the distribution of each root we illustrate our proposed procedure extensively with three examples and evaluate its performance comprehensively through a simulation study

robust constrained model predictive control using linear matrix inequalities abstract the primary disadvantage of current design techniques for model predictive control mpc is their inability to deal explicitly with plant model uncertainty in this paper we present a new approach for robust mpc synthesis that allows explicit incorporation of the description of plant uncertainty in the problem formulation the uncertainty is expressed in both the time and frequency domains the goal is to design at each time step a state feedback control law that minimizes a worst case infinite horizon objective function subject to constraints on the control input and plant output using standard techniques the problem of minimizing an upper bound on the worst case objective function subject to input and output constraints is reduced to a convex optimization involving linear matrix inequalities lmis it is shown that the feasible receding horizon state feedback control design robustly stabilizes the set of uncertain plants several extensions such as application to systems with time delays problems involving constant set point tracking trajectory tracking and disturbance rejection which follow naturally from our formulation are discussed the controller design is illustrated with two examples

stabilization of uncertain fuzzy control systems via a new descriptor system approach this paper addresses stabilizing a class of fuzzy control systems with a guaranteed h control performance via a new descriptor system approach based on the sector nonlinearity concept of tanaka and wang 2001 1 the uncertain nonlinear system can be exactly represented by t s fuzzy models then we propose using the composite state and output feedback csaof fuzzy control for the control design a new descriptor fuzzy system will be represented in this paper based on the lyapunov stability theorem and the linear matrix inequality lmi tool we solve the controller gain matrices some positive constants and some common positive definite matrices then we derive two sufficient conditions to stabilize the uncertain fuzzy control systems with guaranteed h control performance moreover the developed h criterion guarantees that the influence of external disturbance is as small as possible a practical system is given to illustrate the validity of the proposed scheme

semiparametric inferences for association with semi competing risks data in many biomedical studies it is of interest to assess dependence between bivariate failure time data we focus here on a special type of such data referred to as semi competing risks data in this article we develop methods for making inferences regarding dependence of semi competing risks data across strata of a discrete covariate z a class of rank statistics for testing constancy of association across strata are proposed its asymptotic properties are also derived we develop a novel resampling based technique for calculating the variances of the proposed test statistics in addition we develop methods for combining test statistics for assessing marginal effects of z on the dependent censoring variable as well as its effects on association the finite sample properties of the proposed methodology are assessed using simulation studies and they are applied to data from a leukaemia transplantation study copyright 2005 john wiley sons ltd

variable universe stable adaptive fuzzy control of a nonlinear system abstract a kind of stable adaptive fuzzy control of a nonlinear system is implemented based on the variable universe method proposed first in 1 first of all the basic structure of variable universe adaptive fuzzy controllers is briefly introduced then the contraction expansion factor which is a key tool of the variable universe method is defined by means of the integral regulation idea and then a kind of adaptive fuzzy controller is designed by using such a contraction expansion factor the simulation on the first order nonlinear system is done and as a result its simulation effect is quite good in comparison with the corresponding result in 2 3 second it is proved that the variable universe adaptive fuzzy control is asymptotically stable by use of liapunov theory the simulation on a second order nonlinear system shows that its simulation effect is also quite good in comparison with the corresponding result in 2 besides a useful tool called symbolic factor is proposed which may be of universal significance it can greatly reduce the setting time and enhance the robustness of the system

conditional empirical likelihood estimation and inference for quantile regression models abstract this paper considers two empirical likelihood based estimation inference and specification testing methods for quantile regression models first we apply the method of conditional empirical likelihood cel by kitamura et al 2004 empirical likelihood based inference in conditional moment restriction models econometrica 72 1667 1714 and zhang and gijbels 2003 sieve empirical likelihood and extensions of the generalized least squares scandinavian journal of statistics 30 1 24 to quantile regression models second to avoid practical problems of the cel method induced by the discontinuity in parameters of cel we propose a smoothed counterpart of cel called smoothed conditional empirical likelihood scel we derive asymptotic properties of the cel and scel estimators parameter hypothesis tests and model specification tests important features are i the cel and scel estimators are asymptotically efficient and do not require preliminary weight estimation ii by inverting the cel and scel ratio parameter hypothesis tests asymptotically valid confidence intervals can be obtained without estimating the asymptotic variances of the estimators and iii in contrast to cel the scel method can be implemented by some standard newton type optimization simulation results demonstrate that the scel method in particular compares favorably with existing alternatives

a finite time synergetic control scheme for robot manipulators a finite time synergetic control ftsc scheme which is synthesized with synergetic theory and a terminal attractor technique is proposed for controlling robot manipulators unlike conventional synergetic control sc or conventional sliding mode control smc the proposed control scheme has the characteristics of finite time convergence and chattering free phenomena the lyapunov stability method is adopted here to verify the stability of the controlled system the proposed controller is then applied in the control of n link robot manipulators all the simulation results demonstrate the effectiveness and feasibility of the proposed control method

covariate adjustment using propensity scores for dependent censoring problems in the accelerated failure time model in many medical studies estimation of the association between treatment and outcome of interest is often of primary scientific interest standard methods for its evaluation in survival analysis typically require the assumption of independent censoring this assumption might be invalid in many medical studies where the presence of dependent censoring leads to difficulties in analyzing covariate effects on disease outcomes this data structure is called semicompeting risks data for which many authors have proposed an artificial censoring technique however confounders with large variability may lead to excessive artificial censoring which subsequently results in numerically unstable estimation in this paper we propose a strategy for weighted estimation of the associations in the accelerated failure time model weights are based on propensity score modeling of the treatment conditional on confounder variables this novel application of propensity scores avoids excess artificial censoring caused by the confounders and simplifies computation monte carlo simulation studies and application to aids and cancer research are used to illustrate the methodology

nonlinear analysis and control of the uncertain micro electro mechanical system by using a fuzzy sliding mode control design this study analyzes the chaotic behavior of a micromechanical resonator with electrostatic forces on both sides and investigates the control of chaos a phase portrait maximum lyapunov exponent and bifurcation diagram are used to find the chaotic dynamics of this micro electro mechanical system mems to suppress chaotic motion a robust fuzzy sliding mode controller fsmc is designed to turn the chaotic motion into a periodic motion even when the mems has system uncertainties

the highest confidence density region and its usage for joint inferences about constrained parameters suppose that we are interested in making joint inferences about a set of constrained parameters confidence regions for these parameters are often constructed via a normal approximation of the distribution of a consistent estimator for a transformation of the parameters in this article we utilize the confidence distribution a frequentist counterpart to the posterior distribution in bayesian statistics to obtain optimal confidence regions for the parameters members of such a region can be generated efficiently via a standard markov chain monte carlo algorithm we then apply this technique to draw inferences about the temporal profile of the survival function with censored observations we illustrate the new proposal with the survival data from the well known mayo primary biliary cirrhosis study and show that the volume of the new 0 95 confidence region is only one thirty fourth of that of the conventional confidence band

interim analyses based on median survival times summary for a typical two arm survival study analyses are usually performed periodically to evaluate for example the treatment difference in this paper we propose to use differences or ratios of estimated median survival times repeatedly during a study to evaluate the relative merit of two treatment groups this new procedure provides quantitative assessments of the treatment difference and is completely model free our method is illustrated with a recent aids clinical study which evaluates the efficacy of a reduced dose of azt

quantile regression inference with adaptive control of size abstractregression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors this article develops a new estimate of the asymptotic var

semiparametric receiver operating characteristic analysis to evaluate biomarkers for disease the receiver operating characteristic roc curve is a popular method for characterizing the accuracy of diagnostic tests when test results are not binary various methodologies for estimating and comparing roc curves have been developed one approach due to pepe uses a parametric regression model rocx u g h0 u 0x with the baseline function h0 u specified up to a finite dimensional parameter in this article we extend the regression models by allowing arbitrary nonparametric baseline functions we also provide asymptotic distribution theory and procedures for making statistical inference we illustrate our approach with dataset from a prostate cancer biomarker study simulation studies suggest that the extra flexibility inherent in the semiparametric method is gained with little loss in statistical efficiency

nonparametric quantile inference with competing risks data a conceptually simple quantile inference procedure is proposed for cause specific failure probabilities with competing risks data the quantiles are defined using the cumulative incidence function which is intuitively meaningful in the competing risks set up we establish the uniform consistency and weak convergence of a nonparametric estimator of this quantile function these results form the theoretical basis for extensions of standard one sample and two sample quantile inference for independently censored data this includes the construction of confidence intervals and bands for the quantile function and two sample tests simulation studies and a real data example illustrate the practical utility of the methodology

analysis of least absolute deviation summary we develop a unified l 1 based analysis of variance type method for testing linear hypotheses like the classical l2 based analysis of variance the method is coordinate free in the sense that it is invariant under any linear transformation of the covariates or regression parameters moreover it allows singular design matrices and heterogeneous error terms a simple approximation using stochastic perturbation is proposed to obtain cut offvalues for the resulting test statistics both test statistics and distributional approximations can be computed using standard linear programming an asymptotic theory is derived for the method special cases of one and multi way analysis of variance and analysis of covariance models are worked out in detail the main results of this paper can be extended to general quantile regression extensive simulations show that the method works well in practical settings the method is also applied to a dataset from general social surveys

an assumption free exact test for fixed design linear models with exchangeable errors we propose the cyclic permutation test cpt to test general linear hypotheses for linear models this test is non randomized and valid in finite samples with exact type i error alpha for an arbitrary fixed design matrix and arbitrary exchangeable errors whenever 1 alpha is an integer and n p ge 1 alpha 1 the test involves applying the marginal rank test to 1 alpha linear statistics of the outcome vector where the coefficient vectors are determined by solving a linear system such that the joint distribution of the linear statistics is invariant with respect to a non standard cyclic permutation group under the null hypothesis the power can be further enhanced by solving a secondary non linear travelling salesman problem for which the genetic algorithm can find a reasonably good solution extensive simulation studies show that the cpt has comparable power to existing tests when testing for a single contrast of coefficients an exact confidence interval can be obtained by inverting the test furthermore we provide a selective yet extensive literature review of the century long efforts on this problem highlighting the novelty of our test

biometrika centenary nonparametrics summary contributions in biometrika to nonparametric statistics are surveyed with particular emphasis on more recent work it is argued that even in the journal s very early years it had interests in smoothing methods for density estimation and in techniques for nonparametric regression in addition to its more obvious focus on classical nonparametric statistics such as ranking methods biometrika s early involvement with permutation testing has links to its contemporary work on bootstrap methods emphases on different types of nonparametric statistics have evolved over time however a rapidly increasing proportion of the journal s pages is devoted to papers that include some use of nonparametric ideas or methods

confidence bands for survival curves under the proportional hazards model summary in this paper we show how to construct simultaneous confidence bands for the subjectspecific survival curve under the cox proportional hazards model the idea is to approximate the distribution of the normalized cumulative hazard estimator by a zero mean gaussian process whose distribution can be easily generated through simulation numerical studies indicate that the proposed bands are appropriate for practical use a liver disease example is presented

censored median regression using weighted empirical survival and hazard functions abstract for median regression models that regress the median of the survival time or a transform thereof on the covariates some semi parametric estimators that include the intercept component are introduced when the survival time may be censored these new median regression estimators do not require estimating the censoring distributions they can be viewed as an extension of the sample median to the censored regression model these estimators are based on some weighted empirical survival and hazard functions and are shown to be consistent and asymptotically normal they performed very well in various numerical studies the proposed procedures are illustrated in some real data examples

direct use of regression quantiles to construct confidence sets in linear models direct use of the empirical quantile function provides a standard distribution free approach to constructing confidence intervals and confidence bands for population quantiles we apply this method to construct confidence intervals and confidence bands for regression quantiles and to construct prediction intervals based on sample regression quantiles comparison of the direct method with the studentization and the bootstrap methods are discussed simulation results show that the direct method has the advantage of robustness against departure from the normality assumption of the error terms

competing risks quantile regression quantile regression has emerged as a significant extension of traditional linear models and its potential in survival applications has recently been recognized in this paper we study quantile regression with competing risks data formulating the model based on conditional quantiles defined using the cumulative incidence function which includes as a special case an analog to the usual accelerated failure time model the proposed competing risks quantile regression model provides meaningful physical interpretations of covariate effects and moreover relaxes the constancy constraint on regression coefficients thereby providing a useful perhaps more flexible alternative to the popular subdistribution proportional hazards model we derive an unbiased monotone estimating equation for regression parameters in the quantile model the uniform consistency and weak convergence of the resulting estimators are established across a quantile continuum we develop inferences including covariance estimation second

semiparametric regression analysis for doubly censored data summary we analyse doubly censored data using semiparametric transformation models we provide inference procedures for the regression parameters and derive the asymptotic distributions of the proposed estimators procedures for model checking and model selection are also discussed we illustrate our approach with a viral load dataset from a recent aids clinical trial

quantile regression models with multivariate failure time data en tant qu alternative a la classique modelisation de la moyenne par regression la modelisation des quantiles a ete largement etudiee surtout celle de la mediane dans le cas de donnees de survie independantes cependant les correlations naturelles ou artificielles entre donnees font qu il est courant de rencontrer en recherche biomedicale des donnees de survie multivariees pour lesquelles une correlation intra groupe doit etre ajustee pour des donnees de survie correlees et censurees a droite nous explorons ici la modelisation des quantiles et adaptons une approche par equations d estimation destinee a estimer les parametres sous hypothese d independance nous adaptons egalement une version ponderee de cette meme approche qui en ameliore l efficacite nous montrons que les estimateurs des parametres sont robustes et suivent asymptotiquement une distribution normale l estimation de la variance obtenue par approximation asymptotique utilise une estimation non parametrique de la densite cependant que la matrice de variance covariance est estimee par bootstrap et methodes de re echantillonnage perturbe des simulations montrent que l approche proposee convient aux echantillons de taille finie cette nouvelle demarche est appliquee pour illustration a des donnees issues d un essai clinique dans l otite

statistical inference based on non smooth estimating functions summary when the estimating function for a vector of parameters is not smooth it is often rather difficult if not impossible to obtain a consistent estimator by solving the corresponding estimating equation using standard numerical techniques in this paper we propose a simple inference procedure via the importance sampling technique which provides a con sistent root of the estimating equation and also an approximation to its distribution without solving any equations or involving nonparametric function estimates the new proposal is illustrated and evaluated via two extensive examples with real and simulated datasets

multiple imputation for cure rate quantile regression with censored data summary the main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured or whether they are susceptible or insusceptible to the event of interest considering the susceptible indicator as missing data we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction we develop an iterative algorithm to estimate the conditionally uncured probability for each subject by utilizing this estimated probability and bernoulli sample imputation we can classify each subject as cured or uncured and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects repeating the imputation procedure multiple times and taking an average over the resultant estimators we obtain consistent estimators for the quantile regression coefficients our approach relaxes the usual global linearity assumption so that we can apply quantile regression to any particular quantile of interest we establish asymptotic properties for the proposed estimators including both consistency and asymptotic normality we conduct simulation studies to assess the finite sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration

sensorless stator field orientation controlled induction motor drive with a fuzzy speed controller a sensorless stator field oriented control induction motor drive with a fuzzy logic speed controller is presented first a current and voltage parallel model stator flux estimator is established using measured phase currents and voltages of the induction motor then the estimated rotor shaft position is obtained from the magnitude and position of the estimated stator flux the speed controller is developed by utilizing fuzzy logic control techniques the control algorithms are realized by a dsp 6713 and using a dsp f2812 to generate pwm signals to the power stage drive the motor to experimentally validate the proposed approach

comparing two failure time distributions in the presence of dependent censoring summary in a randomised clinical trial to compare two groups of patients suppose that the time to disease occurrence the major response variable may be subject to dependent censoring by death or selective patient withdrawal while the patients who have reached the disease endpoint are followed for their secondary endpoints or survival information to adjust for the dependent censoring in assessing the group difference in disease occurrence we assume that on a logarithmic scale the times to disease occurrence and dependent censoring for the two groups satisfy a bivariate location shift model with a completely unspecified underlying distribution rank based procedures are constructed for making inferences about the location shift parameter model checking techniques are also developed numerical studies show that the proposed methods are appropriate for practical use an illustration with data taken from a recent aids clinical trial is provided

rank estimation of treatment differences based on repeated measurements subject to dependent censoring abstract in comparing the effectiveness of two treatments suppose that for each patient repeated measurements of an outcome variable are taken at prespecified time points but some observations may be missing due to the patient s dependent right censoring in this article a simple rank estimation procedure constructed based on an artificial censoring technique is proposed for the treatment differences over time without imposing a parametric structure on the dependence between the outcome measures and the censoring variable our method can be easily implemented and is illustrated by a dataset from an aids clinical trial

two sample quantile tests under general conditions summary a simple nonparametric two sample test for equality of a given collection of quantiles is developed which can be applied to a variety of empirical distribution functions including the kaplan meier estimator a self consistent estimator for doubly censored data and an estimator for repeated measures data the null hypothesis tested is that the quantiles are equal but other aspects of the distributions may diver between the two samples this procedure can also be applied to quantile testing in group sequential clinical trials with staggered patient entry a simple simulation study demonstrates that the moderate sample size properties of this procedure are reasonable

bayesian analysis of constrained parameter and truncated data problems using gibbs sampling abstract constrained parameter problems arise in a wide variety of applications including bioassay actuarial graduation ordinal categorical data response surfaces reliability development testing and variance component models truncated data problems arise naturally in survival and failure time studies ordinal data models and categorical data studies aimed at uncovering underlying continuous distributions in many applications both parameter constraints and data truncation are present the statistical literature on such problems is very extensive reflecting both the problems widespread occurrence in applications and the methodological challenges that they pose however it is striking that so little of this applied and theoretical literature involves a parametric bayesian perspective from a technical viewpoint this perhaps is not difficult to understand the fundamental tool for bayesian calculations in typical realistic models is multidimensional numerical integration which often is problem

rank based regression for analysis of repeated measures we consider rank based regression models for repeated measures to account for possible within subject correlations we decompose the total ranks into between and within subject ranks and obtain two different estimators based on between and within subject ranks a simple perturbation method is then introduced to generate bootstrap replicates of the estimating functions and the parameter estimates this provides a convenient way for combining the corresponding two types of estimating function for more efficient estimation

calibration regression of censored lifetime medical cost analysis of lifetime medical cost is challenged by its irregular distribution and by incomplete follow up particularly the latter with limited study duration as is typical in medical research this article proposes the so called calibration regression to address these issues by modeling not only lifetime medical cost but also survival time in a semiparametric fashion it is postulated that both outcomes on possibly transformed scales linearly relate to the covariates however the bivariate model error distribution is completely unspecified whereas this conceptually simple model is a natural multivariate generalization of the accelerated failure time model this article proposes an inference procedure by extending the weighted log rank estimating function to the marked point process framework the resulting estimators are consistent and asymptotically normal in addition robustness and efficiency issues are discussed furthermore a novel sample based variance estimation procedure is proposed for es

induced smoothing for rank based regression with recurrent gap time data various semiparametric regression models have recently been proposed for the analysis of gap times between consecutive recurrent events among them the semiparametric accelerated failure time aft model is especially appealing owing to its direct interpretation of covariate effects on the gap times in general estimation of the semiparametric aft model is challenging because the rank based estimating function is a nonsmooth step function as a result solutions to the estimating equations do not necessarily exist moreover the popular resampling based variance estimation for the aft model requires solving rank based estimating equations repeatedly and hence can be computationally cumbersome and unstable in this paper we extend the induced smoothing approach to the aft model for recurrent gap time data our proposed smooth estimating function permits the application of standard numerical methods for both the regression coefficients estimation and the standard error estimation large sample properties and an asymptotic variance estimator are provided for the proposed method simulation studies show that the proposed method outperforms the existing nonsmooth rank based estimating function methods in both point estimation and variance estimation the proposed method is applied to the data analysis of repeated hospitalizations for patients in the danish psychiatric center register

on a general class of semiparametric hazards regression models a general class of semiparametric hazards regression models for survival data is proposed and studied this general class includes some popular classes of models as subclasses such as cox s proportional hazards model the accelerated failure time model and a recently proposed class of models called the accelerated hazards model in the general class of models a covariate s effect is identified as having two separate components namely a time scale change on hazard progression and a relative hazards ratio the new model is flexible in modelling survival data and may yield more accurate prediction of an individual s survival process by way of the nested structure that includes the proportional hazards model the accelerated failure time model and the accelerated hazards model the general class of models may provide a numerical tool for determining which of them is more appropriate for a given dataset

min max optimal control of linear systems with uncertainty and terminal state constraints in this paper a class of min max optimal control problems with continuous dynamical systems and quadratic terminal constraints is studied the main contribution is that the original terminal state constraint in which the disturbance is involved is transformed into an equivalent linear matrix inequality without disturbance under certain conditions then the original min max optimal control problem is solved via solving a sequence of semi definite programming problems an example is presented to illustrate the proposed method

quantile regression for left truncated semicompeting risks data summary semicompeting risks is often encountered in biomedical studies where a terminating event censors a nonterminating event but not vice versa in practice left truncation on the terminating event may arise and can greatly complicate the regression analysis on the nonterminating event in this work we propose a quantile regression method for left truncated semicompeting risks data which provides meaningful interpretations as well as the flexibility to accommodate varying covariate effects we develop estimation and inference procedures that can be easily implemented by existing statistical software asymptotic properties of the resulting estimators are established including uniform consistency and weak convergence the finite sample performance of the proposed method is evaluated via simulation studies an application to a registry dataset provides an illustration of our proposals

control of constrained discrete time systems with bounded ell_ 2 gain we consider the problem of designing a control law for a constrained linear system with bounded disturbances that ensures constraint satisfaction over an infinite horizon while also guaranteeing that the closed loop system has bounded lscr2 gain to this end we propose a receding horizon control strategy based on the repeated calculation of optimal finite horizon feedback policies we parameterize these policies such that the input at each time is an affine function of current and prior states and minimize a worst case quadratic cost where the disturbance energy is negatively weighted as in h infin control we show that the resulting receding horizon controller has two advantages over previous results for this problem first the policy optimization problem to be solved at each time step can be rendered convex concave with a number of decision variables and constraints that grows polynomially with the problem size thereby making its solution amenable to standard techniques in convex optimization second the achievable lscr2 gain of the resulting closed loop system is bounded and non increasing with increasing control horizon a numerical example is included to demonstrate the improvement in achievable lscr2 gain relative to existing methods

statistical inference likelihood to significance abstract the concepts of likelihood and significance were defined and initially developed by r a fisher but followed almost separate and distinct routes we suggest that a central function of statistical inference is in fact the conversion of the first likelihood into the second significance a linking of the fisher concepts a first order asymptotic route for this is incorporated into most statistical packages it uses the standardized maximum likelihood estimate the standardized score or the signed square root of the likelihood ratio statistic as arguments for the standard normal distribution function thus giving approximate tail probabilities or observed levels of significance recent third order asymptotic methods provide a substantial increase in accuracy but need the first derivative dependence of likelihood on the data value as an additional input this can be envisaged as the effect on the likelihood function of dithering the data point extensions to the multivariate multiparameter cont

rank estimation of accelerated lifetime models with dependent censoring under independent censoring estimation of the covariate effects in the accelerated lifetime model may be based on censored data rank tests similar rank methodology has been developed with bivariate accelerated lifetime models for dependent censoring but uses artificial censoring which may lead to substantial information loss we present a new artificial censoring technique using pairwise ranking and establish the asymptotic properties of a pairwise rank estimator simulations show that the pairwise approach achieves large reductions in artificial censoring and large efficiency gains over the existing rank estimator the simulations evidence moderate efficiency gains under independent censoring over a rank estimator that is semiparametric efficient under independent censoring an aids data analysis illustrates the practical utility of the inferential procedures