performance of generalized estimating equations in practical situations moment methods for analyzing repeated binary responses have been proposed by liang and zeger 1986 biometrika 73 13 22 and extended by prentice 1988 biometrics 44 1033 1048 in their generalized estimating equations gee both liang and zeger 1986 and prentice 1988 estimate the parameters associated with the expected value of an individual s vector of binary responses as well as the correlations between pairs of binary responses in this paper we discuss one step estimators i e estimators obtained from one step of the generalized estimating equations and compare their performance to that of the fully iterated estimators in small samples in simulations we find the performance of the one step estimator to be qualitatively similar to that of the fully iterated estimator when the sample size is small and the association between binary responses is high we recommend using the one step estimator to circumvent convergence problems associated with the fully iterated gee algorithm furthermore we find the gee methods to be more efficient than ordinary logistic regression with variance correction for estimating the effect of a time varying covariate

the klein gordon equation of generalized hulthen potential in complex quantum mechanics we have investigated the reality of exact bound states of complex and or pt symmetric non hermitian exponential type generalized hulthen potential the klein gordon equation has been solved by using the nikiforov uvarov method which is based on solving the second order linear differential equations by reduction to a generalized equation of hypergeometric type in many cases of interest negative and positive energy states have been discussed for different types of complex potentials

meboost variable selection in the presence of measurement error we present a novel method for variable selection in regression models when covariates are measured with error the iterative algorithm we propose measurement error boosting meboost follows a path defined by estimating equations that correct for covariate measurement error we illustrate the use of meboost in practice by analyzing data from the box lunch study a clinical trial in nutrition where several variables are based on self report and hence measured with error where we are interested in performing model selection from a large data set to select variables that are related to the number of times a subject binge ate in the last 28 days furthermore we evaluated our method and compared its performance to the recently proposed convex conditioned lasso and to the naive lasso which does not correct for measurement error through a simulation study increasing the degree of measurement error increased prediction error and decreased the probability of accurate covariate selection but this loss of accuracy occurred to a lesser degree when using meboost through simulations we also make a case for the consistency of the model selected

penalized estimating equations les modeles avec penalisation comme l estimateur ridge l estimateur de stein l estimateur en pont et le lasso ont ete proposes pour prendre en compte la collineariteen regression le lasso a ete applique au modele lineaire a la regression logistique au modele des hasards proportionnels de cox et aux reseaux de neurones cet article etudie le modele avec penalisation en pont pour une penalite j j applique aux equations d estimation et applique ce modele avec penalisation aux equations d estimation generalisees gee dans des etudes longitudinales le defaut de vraisemblance jointe dans les gee est surmonte par les equations d estimation penalisees pour lesquelles aucune vraisemblance jointe n est requise on donne les resultats asymptotiques pour l estimateur sons penalite on montre par des simulations et une application que les cee avec penalisation ameliorent potentiellement les resultats de l estimateur gee et presentent les memes proprietes que les modeles lineaires penalises

a projected likelihood function for semiparametric models summary in a sequence of papers mcleish 1984 hutton nelson 1986 and godambe heyde 1987 have motivated the use of the quasi score function as an estimating function for semiparametric models by showing that it is the projection of the true score into a class of unbiased estimating functions thus the quasi score approximates the score within this class of functions in this paper we introduce a class of inference functions for independent observations that contain analogues of likelihood ratios under semiparametric assumptions this class of functions which contains the semiparametric estimating functions as a subspace is built through the use of tensor products for models with independent observations we consider the projection of the likelihood ratios into this class of inference functions it is shown that this projected likelihood is first order equivalent to the quasi likelihood

pt symmetric pseudo perturbation recipe an imaginary cubic oscillator with spikes the pseudo perturbative shifted l expansion technique pslet is shown to be applicable in the non hermitian symmetric context the construction of bound states for several symmetric potentials is presented with special attention paid to v r ir3 ir oscillators

random fibonacci sequences solutions to the random fibonacci recurrence xn 1 xn xn 1 decrease increase exponentially xn exp n for sufficiently small large in the limits 0 and we expand the lyapunov exponent in powers of and 1 respectively for the classical case of 1 we obtain exact non perturbative results in particular an invariant measure associated with ricatti variable rn xn 1 xn is shown to exhibit plateaux around all rational r

boosting with the l2 loss this article investigates a computationally simple variant of boosting l2boost which is constructed from a functional gradient descent algorithm with the l2 loss function like other boosting algorithms l2boost uses many times in an iterative fashion a prechosen fitting method called the learner based on the explicit expression of refitting of residuals of l2boost the case with symmetric linear learners is studied in detail in both regression and classification in particular with the boosting iteration m working as the smoothing or regularization parameter a new exponential bias variance trade off is found with the variance complexity term increasing very slowly as m tends to infinity when the learner is a smoothing spline an optimal rate of convergence result holds for both regression and classification and the boosted smoothing spline even adapts to higher order unknown smoothness moreover a simple expansion of a smoothed 0 1 loss function is derived to reveal the importance of the d

local enrichment and its nonlocal consequences for victim exploiter metapopulations abstract the stabilizing effects of local enrichment are revisited diffusively coupled host parasitoid and predator prey metapopulations are shown to admit a stable fixed point limit cycle or stable torus with a rich bifurcation structure a linear toy model that yields many of the basic qualitative features of this system is presented the further nonlinear complications are analyzed in the framework of the marginally stable lotka volterra model and the continuous time analog of the unstable host parasitoid nicholson bailey model the dependence of the results on the migration rate and level of spatial variations is examined and the possibility of nonlocal effect of enrichment where local enrichment induces stable oscillations at a distance is studied a simple method for basic estimation of the relative importance of this effect in experimental systems is presented and exemplified

double semiparametric method for missing covariates in cox regression models the problem of nuisance covariate model specification is considered in cox regression where the maximum semiparametric likelihood method is used to handle the missing covariates a component of the covariates is modeled nonparametrically to achieve robustness against covariate model misspecification and to reduce the number of possibly intractable integrations involved in the parametric modeling of the covariates the statistical properties of the proposed method are examined it is found that in some important situations the maximum semiparametric likelihood can be applied without making any additional parametric model assumptions on covariates the proposed method can yield a more efficient estimator than the nonparametric imputation methods and does not require specification of the missingness mechanism when compared with the inverse probability weighting method a real data example is analyzed to demonstrate use of the proposed method

eeboost a general method for prediction and variable selection based on estimating equations the modern statistical literature is replete with methods for performing variable selection and prediction in standard regression problems however simple models may misspecify or fail to capture important aspects of the data generating process such as missingness correlation and over underdispersion in this article we describe eeboost a strategy for variable selection and prediction which can be applied in high dimensional settings where inference for low dimensional parameters would typically be based on estimating equations the method is simple flexible and easily implemented using existing software the eeboost algorithm is obtained as a modification of the standard boosting or functional gradient descent technique we show that eeboost is closely related to a class of l1 constrained projected likelihood ratio minimizations and therefore produces similar variable selection paths to penalized methods without the need to apply constrained optimization algorithms the flexibility of eeboost is

large order perturbation theory for a non hermitian pt symmetric hamiltonian a precise calculation of the ground state energy of the complex pt symmetric hamiltonian h p2 14x2 i x3 is performed using high order rayleigh schrodinger perturbation theory the energy spectrum of this hamiltonian has recently been shown to be real using numerical methods here we present convincing numerical evidence that the rayleigh schrodinger perturbation series is borel summable and show that pade summation provides excellent agreement with the real energy spectrum pade analysis provides strong numerical evidence that the once subtracted ground state energy considered as a function of 2 is a stieltjes function the analyticity properties of this stieltjes function lead to a dispersion relation that can be used to compute the imaginary part of the energy for the related real but unstable hamiltonian h p2 14 x2 ex3

imputation and variable selection in linear regression models with missing covariates across multiply imputed data sets variable selection methods such as stepwise regression and other criterion based strategies that include or exclude particular variables typically result in models with different selected predictors thus presenting a problem for combining the results from separate complete data analyses here drawing on a bayesian framework we propose two alternative strategies to address the problem of choosing among linear regression models when there are missing covariates one approach which we call impute then select its involves initially performing multiple imputation and then applying bayesian variable selection to the multiply imputed data sets a second strategy is to conduct bayesian variable selection and missing data imputation simultaneously within one gibbs sampling process which we call simultaneously impute and select sias the methods are implemented and evaluated using the bayesian procedure known as stochastic search variable selection for multivariate normal data sets but both strategies offer general frameworks within which different bayesian variable selection algorithms could be used for other types of data sets a study of mental health services utilization among children in foster care programs is used to illustrate the techniques simulation studies show that both its and sias outperform complete case analysis with stepwise variable selection and that sias slightly outperforms its

variable selection for cox s proportional hazards model and frailty model a class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed in fan and li 2001a it has been shown there that the resulting procedures perform as well as if the subset of significant variables were known in advance such a property is called an oracle property the proposed procedures were illustrated in the context of linear regression robust linear regression and generalized linear models in this paper the nonconcave penalized likelihood approach is extended further to the cox proportional hazards model and the cox proportional hazards frailty model two commonly used semi parametric models in survival analysis as a result new variable selection procedures for these two commonly used models are proposed it is demonstrated how the rates of convergence depend on the regularization parameter in the penalty function further with a proper choice of the regularization parameter and the penalty function the proposed estimators possess an oracle property standard error formulae are derived and their accuracies are empirically tested simulation studies show that the proposed procedures are more stable in prediction and more effective in computation than the best subset variable selection and they reduce model complexity as effectively as the best subset variable selection compared with the lasso which is the penalized likelihood method with the l 1 penalty proposed by tibshirani the newly proposed approaches have better theoretic properties and finite sample performance

l1 regularization path algorithm for generalized linear models summary we introduce a path following algorithm for l1 regularized generalized linear models the l1 regularization procedure is useful especially because it in effect selects variables according to the amount of penalization on the l1 norm of the coefficients in a manner that is less greedy than forward selection backward deletion the generalized linear model path algorithm efficiently computes solutions along the entire regularization path by using the predictor corrector method of convex optimization selecting the step length of the regularization parameter is critical in controlling the overall accuracy of the paths we suggest intuitive and flexible strategies for choosing appropriate values we demonstrate the implementation with several simulated and real data sets

coevolutionary genetic fuzzy systems a hierarchical collaborative approach abstract in this paper a coevolutionary genetic approach is devised to support hierarchical collaborative relations between individuals representing different parameters of takagi sugeno fuzzy models the coevolutionary approach assumes species to mean partial solutions of fuzzy modeling problems organized into four hierarchical levels individuals at each hierarchical level encode membership functions individual rules rule bases and fuzzy systems respectively a shared fitness evaluation scheme is used to measure the performance of each individual constraints are observed and particular targets are defined throughout the hierarchical levels with the purpose of promoting the occurrence of valid individuals and inducing rule compactness rule base consistency and partition set visibility the performance of the approach is evaluated via an example of function approximation with noisy data and a nonlinearly separable classification problem

the lasso method for variable selection in the cox model summary i propose a new method for variable selection and shrinkage in cox s proportional hazards model my proposal minimizes the log partial likelihood subject to the sum of the absolute values of the parameters being bounded by a constant because of the nature of this constraint it shrinks coe cients and produces some coe cients that are exactly zero as a result it reduces the estimation variance while providing an interpretable final model the method is a variation of the lasso proposal of tibshirani designed for the linear regression context simulations indicate that the lasso can be more accurate than stepwise selection in this setting

model selection in estimating equations summary model selection is a necessary step in many practical regression analyses but for methods based on estimating equations such as the quasi likelihood and generalized estimating equation gee approaches there seem to be few well studied model selection techniques in this article we propose a new model selection criterion that minimizes the expected predictive bias epb of estimating equations a bootstrap smoothed cross validation bcv estimate of epb is presented and its performance is assessed via simulation for overdispersed generalized linear models for illustration the method is applied to a real data set taken from a study of the development of ewe embryos

quantum mechanics of complex hamiltonian systems in one dimension with a view to obtaining further insight into the nature of eigenvalues and eigenfunctions of a stationary state one dimensional schrodinger equation corresponding to a non hermitian hamiltonian h x p we investigate the ground state solutions for a variety of potentials within the framework of an extended complex phase space characterized by x x1 ip2 p p1 ix2 where x1 p1 and x2 p2 are real and considered as canonical pairs the analyticity property of the eigenfunction alone is found sufficient to throw light on the nature of eigenvalues and eigenfunctions for different systems it is noted that the imaginary part of the eigenvalue ei turns out to be zero for all potentials v x with real couplings whereas it turns out to be nonzero for the case when the couplings are complex the prescription is also extended to study the excited states the problems related to the normalization of the eigenfunction and the boundary conditions to be used within this framework are also discussed

additive logistic regression a statistical view of boosting boosting is one of the most important recent developments in classification methodology boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced for many classification algorithms this simple strategy results in dramatic improvements in performance we show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles namely additive modeling and maximum likelihood for the two class problem boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum bernoulli likelihood as a criterion we develop more direct approximations and show that they exhibit nearly identical results to boosting direct multiclass generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multiclass generalizations of boosting in most situations and far superior in some we suggest a minor modification to boosting that can reduce computation often by factors of 10 to 50 finally we apply these insights to produce an alternative formulation of boosting decision trees this approach based on best first truncated tree induction often leads to better performance and can provide interpretable descriptions of the aggregate decision rule it is also much faster computationally making it more suitable to large scale data mining applications

non hermitian localization and population biology the time evolution of spatial fluctuations in inhomogeneous d dimensional biological systems is analyzed a single species continuous growth model in which the population disperses via diffusion and convection is considered time independent environmental heterogeneities such as a random distribution of nutrients or sunlight are modeled by quenched disorder in the growth rate linearization of this model of population dynamics shows that the fastest growing localized state dominates in a time proportional to a power of the logarithm of the system size using an analogy with a schr odinger equation subject to a constant imaginary vector potential we propose a delocalization transition for the steady state of the nonlinear problem at a critical convection threshold separating localized and extended states in the limit of high convection velocity the linearized growth problem in d dimensions exhibits singular scaling behavior described by a d ensuremath 1 dimensional generalization of the noisy burgers equation with universal singularities in the density of states associated with disorder averaged eigenvalues near the band edge in the complex plane the burgers mapping leads to unusual transverse spreading of convecting delocalized populations

on using the cox proportional hazards model with missing covariates summary we propose two methods for handling missing covariates in using the cox proportional hazards model the maximum partial likelihood estimator based only on study subjects having complete covariates does not utilise all available information also it is biased when the probability of missingness depends on the failure or censoring time our suggestion is to impute the conditional expectation of the statistic involving missing covariates given the available information the proposed method provides a consistent regression parameter estimator when the probability of missingness depends on the failure or censoring time as well as on the observed covariates also the proposed estimator is more efficient than the estimator suggested previously by lin ying 1993 when data are missing completely at random

boosting as a regularized path to a maximum margin classifier in this paper we study boosting methods from a new perspective we build on recent work by efron et al to show that boosting approximately and in some cases exactly minimizes its loss criterion with an l1 constraint on the coefficient vector this helps understand the success of boosting with early stopping as regularized fitting of the loss criterion for the two most commonly used criteria exponential and binomial log likelihood we further show that as the constraint is relaxed or equivalently as the boosting iterations proceed the solution converges in the separable case to an l1 optimal separating hyper plane we prove that this l1 optimal separating hyper plane has the property of maximizing the minimal l1 margin of the training data as defined in the boosting literature an interesting fundamental similarity between boosting and kernel support vector machines emerges as both can be described as methods for regularized optimization in high dimensional predictor space using a computational trick to make the calculation practical and converging to margin maximizing solutions while this statement describes svms exactly it applies to boosting only approximately

distribution of eigenvalues in non hermitian anderson models we develop a theory which describes the behavior of eigenvalues of a class of one dimensional random non hermitian operators introduced recently by hatano and nelson we prove that the eigenvalues are distributed along a curve in the complex plane an equation for the curve is derived and the density of complex eigenvalues is found in terms of spectral characteristics of a reference hermitian disordered system the generic properties of the eigenvalue distribution are discussed

can spectral value sets of toeplitz band matrices jump abstract the spectral value set speb ca of a bounded linear operator a on l2 is the union of the spectra of all operators of the form a bkc where k

generalized swanson models and their solutions we analyse a class of non hermitian quadratic hamiltonians which are of the form where are real constants with and and are generalized creation and annihilation operators respectively thus these hamiltonians may be classified as generalized swanson models it is shown that the eigenenergies are real for a certain range of values of the parameters a similarity transformation mapping the non hermitian hamiltonian h to a hermitian one h is also obtained it is shown that h and h share identical energies as explicit examples the solutions of a couple of models based on the trigonometric rosen morse i and the hyperbolic rosen morse ii type potentials are obtained we also study the case where the non hermitian hamiltonian is symmetric

greedy function approximation a gradient boosting machine function estimation approximation is viewed from the perspective of numerical optimization in function space rather than parameter space a connection is made between stagewise additive expansions and steepest descent minimization a general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion specific algorithms are presented for least squares least absolute deviation and huber m loss functions for regression and multiclass logistic likelihood for classification special enhancements are derived for the particular case where the individual additive components are regression trees and tools for interpreting such treeboost models are presented gradient boosting of regression trees produces competitive highly robust interpretable procedures for both regression and classification especially appropriate for mining less than clean data connections between this approach and the boosting methods of freund and shapire and friedman hastie and tibshirani are discussed

penalized estimating functions and variable selection in semiparametric regression models we propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors unlike the existing penalized maximum likelihood estimators the proposed penalized estimating functions may not pertain to the derivatives of any objective functions and may be discrete in the regression coefficients we establish a general asymptotic theory for penalized estimating functions and present suitable numerical algorithms to implement the proposed estimators in addition we develop a resampling technique to estimate the variances of the estimated regression coefficients when the asymptotic variances cannot be evaluated directly simulation studies demonstrate that the proposed methods perform well in variable selection and variance estimation we illustrate our methods using data from the paul coverdell stroke registry

augmented inverse probability weighted estimator for cox missing covariate regression summary this article investigates an augmented inverse selection probability weighted estimator for cox regression parameter estimation when covariate variables are incomplete this estimator extends the horvitz and thompson 1952 journal of the american statistical association 47 663 685 weighted estimator this estimator is doubly robust because it is consistent as long as either the selection probability model or the joint distribution of covariates is correctly specified the augmentation term of the estimating equation depends on the baseline cumulative hazard and on a conditional distribution that can be implemented by using an em type algorithm this method is compared with some previously proposed estimators via simulation studies the method is applied to a real example

the diffusion reaction d r hamiltonian and the solutions of certain types of linear and nonlinear d r equations in one dimension with a view to having further insight into the mathematical content of the non hermitian hamiltonian associated with the diffusion reaction d r equation in one dimension we investigate a the solitary wave solutions of certain types of its nonlinear versions and b the problem of real eigenvalue spectrum associated with its linear version or with this class of non hermitian hamiltonians for case a we use the standard techniques to handle the quadratic and cubic nonlinearities in the d r equation whereas for case b a newly proposed method based on an extended complex phase space is employed for a particular class of solutions an ermakov system of equations is also found for the linear case further corresponding to the classical version of the above one dimensional complex hamiltonian an equivalent integrable system of two two dimensional real hamiltonians is suggested

cox regression with incomplete covariate measurements abstract this article provides a general solution to the problem of missing covariate data under the cox regression model the estimating function for the vector of regression parameters is an approximation to the partial likelihood score function with full covariate measurements and reduces to the pseudolikelihood score function of self and prentice in the special setting of case cohort designs the resulting parameter estimator is consistent and asymptotically normal with a covariance matrix for which a simple and consistent estimator is provided extensive simulation studies show that the large sample approximations are adequate for practical use the proposed approach tends to be more efficient than the complete case analysis especially for large cohorts with infrequent failures for case cohort designs the new methodology offers a variance covariance estimator that is much easier to calculate than the existing ones and allows multiple subcohort augmentations to improve efficiency real data taken f

longitudinal data analysis using generalized linear models summary this paper proposes an extension of generalized linear models to the analysis of longitudinal data we introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence the estimating equations are derived without specifying the joint distribution of a subject s observations yet they reduce to the score equations for multivariate gaussian outcomes asymptotic theory is presented for the general class of estimators specific cases in which we assume independence m dependence and exchangeable correlation structures from each subject are discussed efficiency of the proposed estimators in two simple situations is considered the approach is closely related to quasi likelih ood some key ironh estimating equation generalized linear model longitudinal data quasi likelihood repeated measures

disordered contact process with asymmetric spreading an asymmetric variant of the contact process where the activity spreads with different and independent random rates to the left and to the right is introduced a real space renormalization scheme is formulated for the model by means of which it is shown that the local asymmetry of spreading is irrelevant on large scales if the model is globally statistically symmetric otherwise in the presence of a global bias in either direction the renormalization method predicts two distinct phase transitions which are related to the spreading of activity in and against the direction of the bias the latter is found to be described by an infinite randomness fixed point while the former is not