cox s regression model for counting processes a large sample study the cox regression model for censored survival data specifies that covariates have a proportional effect on the hazard function of the life time distribution of an individual in this paper we discuss how this model can be extended to a model where covariate processes have a proportional effect on the intensity process of a multivariate counting process this permits a statistical regression analysis of the intensity of a recurrent event allowing for complicated censoring patterns and time dependent covariates furthermore this formulation gives rise to proofs with very simple structure using martingale techniques for the asymptotic properties of the estimators from such a model finally an example of a statistical analysis is included

adaptive mesh and algorithm refinement using direct simulation monte carlo adaptive mesh and algorithm refinement amar embeds a particle method within a continuum method at the finest level of an adaptive mesh refinement amr hierarchy the coupling between the particle region and the overlaying continuum grid is algorithmically equivalent to that between the fine and coarse levels of amr direct simulation monte carlo dsmc is used as the particle algorithm embedded within a godunov type compressible navier stokes solver several examples are presented and compared with purely continuum calculations

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

variable selection in semiparametric linear regression with censored data summary we describe two procedures for selecting variables in the semiparametric linear regression model for censored data one procedure penalizes a vector of estimating equations and simultaneously estimates regression coefficients and selects submodels a second proce dure controls systematically the proportion of unimportant variables through forward selection and the addition of pseudorandom variables we explore both rank based statistics and buckley james statistics in the setting proposed and evaluate the performance of all methods through extensive simulation studies and one real data set

accelerated hazards regression model and its adequacy for censored survival data summary the accelerated hazards regression model is introduced to study the relationship between survival times and covariates through a scale change between hazard functions the model is also compared with several other popular classes of regression models for censored survival data in statistical literature test statistics are proposed and studied to assess the model s adequacy actual data from a randomized clinical trial of biodegradable carmustine polymer for treatment of brain cancer are analyzed to demonstrate the potential application of the regression model and the proposed test statistics

regression with censored data summary there are four regression techniques currently available for use with censored data which do not assume particular parametric families of survival distributions they are due to i cox 1972 ii miller 1976 iii buckley james 1979 and iv koul susarla van ryzin 1981 these four methods are described and their performances compared on the updated stanford heart transplant data conclusions on the usefulness of the four procedures are drawn

asymptotic properties of censored linear rank tests a conjecture of prentice is established which states that for censored linear rank test exact scores based on conditional expectations can be replaced by approximate scores obtained by evaluating the score function at an estimate of the survival function we show that under minimal conditions asymptotically equivalent tests are obtained when either the kaplan meier altshuler or moment estimator of the survival function is used asymptotic normality is also established for a general random censorship model under the null hypothesis and for contiguous alternatives this is used to calculate efficacies and when the censoring times are i i d an expression for the asymptotic relative efficiency is given which is a natural generalization of the one for classical uncensored linear rank tests 1 introduction prentice 1978 has constructed a general class of linear rank tests for a regression model with censored data his approach was to specify a score function for a classical linear rank test in the absence of censoring chernoff and savage 1958 hajek and sidak 1967 to construct all possible rankings of the unobserved uncensored values which were consistent with observed censored sample and then to assign to each observation the average of all possible scores it could have received in the absence of censoring giving equal weighting to each possible uncensored ranking except for a few special scoring functions notably logarithmic or power law scores these averages are unwieldy when there is appreciable censoring and prentice suggested that asymptotically equivalent tests might arise if the score function was evaluated at some suitable estimate of the survivor function this is well known to be true for uncensored data but requires further justification when censoring is present because the estimate of the survivor function is more complicated in this work we show that prentice s conjecture is true under minimal conditions and also we show that it remains true if prentice s moment estimator of the survivor function is replaced by either the kaplan meier estimator or altshuler s estimator asymptotic normality of these tests is established under a random censorship model for the null hypothesis and contiguous alternatives by martingale methods this is used to compute efficacies for a general random censorship model and asymptotic relative efficiencies for i i d potential censoring times where fully efficient linear rank tests exist asymptotic normality for general alternatives has been established in cuzick 1982 by more complicated methods

semiparametric approach to regression with a covariate subject to a detection limit we consider generalized linear regression with a covariate left censored at a lower detection limit complete case analysis where observations with values below the limit are eliminated yields valid estimates for regression coefficients but loses efficiency ad hoc substitution methods are biased and parametric maximum likelihood estimation relies on parametric models for the unobservable tail probability distribution and may suffer from model misspecification to obtain robust and more efficient results we propose a semiparametric likelihood based approach using an accelerated failure time model for the covariate subject to the detection limit a two stage estimation procedure is developed where the conditional distribution of this covariate given other variables is estimated prior to maximizing the likelihood function the proposed method outperforms complete case analysis and substitution methods in simulation studies technical conditions for desirable asymptotic properties are provided

hybrid continuum molecular simulations of transient gas flows with rarefaction an adaptively coupled continuum molecular approach for compressible viscous flows in transient calculations is presented the continuum domain is described by the unsteady compressible navier stokes equations and the molecular domain is solved by direct simulation monte carlo a strategy is described to extend the overlapped schwarz method with dirichlet dirichlet boundary condition coupling procedure to transient simulations the method has been successfully validated against full direct simulation monte carlo results for transient simulations of a one dimensional shock tube and a two dimensional pressure driven slit flow a sensitivity analysis showed that the used overlapped schwarz coupling method with dirichlet dirichlet boundary conditions is only weakly sensitive to various parameters e g the exact position of the continuum molecular interface the size of the overlap region and the scatter in the molecular solution which is a clear advantage over the more commonly used flux based coupling technique another advantage of the chosen coupling method is that the continuum and molecular time steps can be decoupled and that in general a coupling time step can be used that is much larger than the molecular time step the study performed also highlighted a limit of the method it was found to be necessary to keep the courant number based on the coupling time step the cell size in the continuum region and the molecular most probable velocity below 1 to avoid instabilities

prediction of cumulative incidence function under the proportional hazards model in the presence of dependent competing risks in survival analysis the cox model can be utilized to examine the covariate effects on the cause specific hazard function for the failure type of interest for this situation the cumulative incidence function provides an intuitively appealing summary curve for marginal probabilities of this particular event in this paper we show how to construct confidence intervals and bands for such a function under the cox model for future patients with certain covariates our proposals are illustrated with data from a prostate cancer trial

generation of the chapman enskog distribution a pair of simple efficient and robust algorithms for generating random velocities sampled from the chapman enskog distribution is presented

development and verification of a coupled dsmc ns scheme using unstructured mesh an efficient and accurate parallel coupled dsmc ns method using three dimensional unstructured grid topology is proposed and verified for the simulation of high speed gas flows involving continuum and rarefied regimes a domain overlapping strategy taking advantage of unstructured data format with dirichlet dirichlet type boundary conditions based on two breakdown parameters is used iteratively to determine the choice of solvers in the spatial domain the selected breakdown parameters for this study include 1 a local maximum knudsen number defined as the ratio of the local mean free path and local characteristic length based on property gradient and 2 a thermal non equilibrium indicator defined as the ratio of the difference between translational and rotational temperatures to the translational temperature a supersonic flow m 4 over a quasi 2 d 25 wedge is employed as the first step in verifying the present coupled method the results of simulation using the coupled method are in excellent agreement with those of the pure dsmc method which is taken as the benchmark solution effects of the size of overlapping regions and the choice of breakdown parameters on the convergence history are discussed results show that the proposed iteratively coupled method predicts the results more accurately as compared to the one shot coupled method which has been often used in practice further a realistic 3 d nitrogen flow which two near continuum parallel orifice jets underexpand into a near vacuum environment is simulated using the present coupled method to demonstrate its capability finally developments in extending the present method are also outlined in this paper

linear regression analysis of censored survival data based on rank tests summary recently linear rank statistics with censored data have been used as the estimating functions for the regression parameters in the linear model with an unspecified error distribution the resulting rank estimators are consistent and asymptotically normal however the asymptotic variances of these estimators are complicated and are difficult to estimate well with censored data in this paper we propose some simple methods for making inference about a subset of the regression coefficients while regarding others as nuisance parameters a lack of fit test for the linear model is also presented the proposed procedures are illustrated with an example

an h adaptive mesh method for boltzmann bgk hydrodynamics coupling we introduce a coupled method for hydrodynamic and kinetic equations on 2 dimensional h adaptive meshes we adopt the euler equations with a fast kinetic solver in the region near thermodynamical equilibrium while use the boltzmann bgk equation in kinetic regions where fluids are far from equilibrium a buffer zone is created around the kinetic regions on which a gradually varying numerical flux is adopted based on the property of a continuously discretized cut off function which describes how the flux varies the coupling will be conservative in order for the conservative 2 dimensional specularly reflective boundary condition to be implemented conveniently the discrete maxwellian is approximated by a high order continuous formula with improved accuracy on a disc instead of on a square domain the h adaptive method can work smoothly with a time split numerical scheme through h adaptation the cell number is greatly reduced this method is particularly suitable for problems with hydrodynamics breakdown on only a small part of the whole domain so that the total efficiency of the algorithm can be greatly improved three numerical examples are presented to validate the proposed method and demonstrate its efficiency

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

a general semiparametric hazards regression model efficient estimation and structure selection we consider a general semiparametric hazards regression model that encompasses the cox proportional hazards model and the accelerated failure time model for survival analysis to overcome the nonexistence of the maximum likelihood we derive a kernel smoothed profile likelihood function and prove that the resulting estimates of the regression parameters are consistent and achieve semiparametric efficiency in addition we develop penalized structure selection techniques to determine which covariates constitute the accelerated failure time model and which covariates constitute the proportional hazards model the proposed method is able to estimate the model structure consistently and model parameters efficiently furthermore variance estimation is straightforward the proposed estimation performs well in simulation studies and is applied to the analysis of a real data set

applications of multiple imputation to the analysis of censored regression data the first part of the article reviews the data augmentation algorithm and presents two approximations to the data augmentation algorithm for the analysis of missing data problems the poor man s data augmentation algorithm and the asymptotic data augmentation algorithm these two algorithms are then implemented in the context of censored regression data to obtain semiparametric methodology the performances of the censored regression algorithms are examined in a simulation study it is found up to the precision of the study that the bias of both the poor man s and asymptotic data augmentation estimators as well as the buckley james estimator does not appear to differ from zero however with regard to mean squared error over a wide range of settings examined in this simulation study the two data augmentation estimators have a smaller mean squared error than does the buckley james estimator in addition associated with the two data augmentation estimators is a natural device for estimating the standard error of the estimated regression parameters it is shown how this device can be used to estimate the standard error of either data augmentation estimate of any parameter e g the correlation coefficient associated with the model in the simulation study the estimated standard error of the asymptotic data augmentation estimate of the regression parameter is found to be congruent with the monte carlo standard deviation of the corresponding parameter estimate the algorithms are illustrated using the updated stanford heart transplant data set

regression analysis with randomly right censored data this paper proposes a new estimator of the parameter vector in a linear regression model when the observations are randomly censored on the right and when the error distribution is unknown this estimator is explicitly defined and easily computable the paper contains sufficient conditions under which this estimator is mean square consistent and asymptotically normal a numerical example is given

nonparametric estimation for a scale change with censored observations abstract nonparametric point and interval estimators of the ratio of two scale parameters are given for arbitrarily right censored data based on the idea of hodges and lehmann 1963 these estimators are defined in terms of rank test statistics for testing the equality of two survival distributions the asymptotic properties and efficiencies of estimators corresponding to the tests studied by gill 1980 are investigated

flow simulation using generalized static and dynamic grids a new approach is presented for a e ow simulation system using generalized grids in a generalized grid the physical domain of interest is decomposed into cells with an arbitrary number of edges or faces the grid can be of structured unstructured or hanging node type or an arbitrary combination of the types a cell face based data structure is used to storethegrid information a e ow simulation system is developed forgeneralized gridsthat can handle static and dynamic grids the full navier stokes equations in the integral form are taken as the relations that govern the e uid e ow a cell centered e nite volume scheme is developed for solving these governing equations the numerical e ux across the cell faces is evaluated by an upwind scheme based on roe s approximate riemann solver a higher order scheme is formulated by utilizing taylor s series expansion and green s theorem limiter functions are used to preserve monotonocity the skin friction coefe cient is used to evaluate the accuracy of the limiterfunctions thegeneralized minimalresidualmethod isutilized tosolvethesparselinearsystem ofequations resulting from thelinearization of the e ux vectors thespalart allmaras one equation turbulence model has been implemented for the generalized grids and is used to evaluate the turbulent viscosity for dynamically moving bodies the equations of classical mechanics are used to predict the trajectory based on the external aerodynamic and body forces a variety of computational examples are presented to demonstratethe wide rangeof applications and the results are compared with experimental data whenever available

a hybrid method for hydrodynamic kinetic flow part i a particle grid method for reducing stochastic noise in kinetic regimes in this work we present a hybrid particle grid monte carlo method for the boltzmann equation which is characterized by a significant reduction of the stochastic noise in the kinetic regime the hybrid method is based on a first order splitting in time to separate the transport from the relaxation step the transport step is solved by a deterministic scheme while a hybrid dsmc based method is used to solve the collision step such a hybrid scheme is based on splitting the solution in a collisional and a non collisional part at the beginning of the collision step and the dsmc method is used to solve the relaxation step for the collisional part of the solution only this is accomplished by sampling only the fraction of particles candidate for collisions from the collisional part of the solution performing collisions as in a standard dsmc method and then projecting the particles back onto a velocity grid to compute a piecewise constant reconstruction for the collisional part of the solution the latter is added to a piecewise constant reconstruction of the non collisional part of the solution which in fact remains unchanged during the relaxation step numerical results show that the stochastic noise is significantly reduced at large knudsen numbers with respect to the standard dsmc method indeed in this algorithm the particle scheme is applied only on the collisional part of the solution so only this fraction of the solution is affected by stochastic fluctuations but since the collisional part of the solution reduces as the knudsen number increases stochastic noise reduces as well at large knudsen numbers

quantile regression for doubly censored data double censoring often occurs in registry studies when left censoring is present in addition to right censoring in this work we propose a new analysis strategy for such doubly censored data by adopting a quantile regression model we develop computationally simple estimation and inference procedures by appropriately using the embedded martingale structure asymptotic properties including the uniform consistency and weak convergence are established for the resulting estimators moreover we propose conditional inference to address the special identifiability issues attached to the double censoring setting we further show that the proposed method can be readily adapted to handle left truncation simulation studies demonstrate good finite sample performance of the new inferential procedures the practical utility of our method is illustrated by an analysis of the onset of the most commonly investigated respiratory infection pseudomonas aeruginosa in children with cystic fibrosis through the use of the u s cystic fibrosis registry

a sieve m theorem for bundled parameters in semiparametric models with application to the efficient estimation in a linear model for censored data in many semiparametric models that are parameterized by two types of parameters a euclidean parameter of interest and an infinite dimensional nuisance parameter the two parameters are bundled together i e the nuisance parameter is an unknown function that contains the parameter of interest as part of its argument for example in a linear regression model for censored survival data the unspecified error distribution function involves the regression coefficients motivated by developing an efficient estimating method for the regression parameters we propose a general sieve m theorem for bundled parameters and apply the theorem to deriving the asymptotic theory for the sieve maximum likelihood estimation in the linear regression model for censored survival data the numerical implementation of the proposed estimating method can be achieved through the conventional gradient based search algorithms such as the newton raphson algorithm we show that the proposed estimator is consistent and asymptotically normal and achieves the semiparametric efficiency bound simulation studies demonstrate that the proposed method performs well in practical settings and yields more efficient estimates than existing estimating equation based methods illustration with a real data example is also provided

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

score estimating equations from embedded likelihood functions under accelerated failure time model the semiparametric accelerated failure time aft model is one of the most popular models for analyzing time to event outcomes one appealing feature of the aft model is that the observed failure time data can be transformed to identically independent distributed random variables without covariate effects we describe a class of estimating equations based on the score functions for the transformed data which are derived from the full likelihood function under commonly used semiparametric models such as the proportional hazards or proportional odds model the methods of estimating regression parameters under the aft model can be applied to traditional right censored survival data as well as more complex time to event data subject to length biased sampling we establish the asymptotic properties and evaluate the small sample performance of the proposed estimators we illustrate the proposed methods through applications in two examples

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

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

new lifting relations for estimating lbm distribution functions from corresponding macroscopic quantities based on equilibrium and non equilibrium moments due to superior accuracy and stability of multiple relaxation time mrt collision operator over its single relaxation time srt counterpart new lifting relations are proposed here to construct single particle distribution functions for mrt lbm from macroscopic variables using these lifting relations a new hybrid fvm lb method is presented called finite type lb hybrid method which is consistent with mrt lbm in this new hybrid method single particle distribution functions in mrt lbm sub domain boundaries are computed using equilibrium and non equilibrium moments these moments are computed in navier stokes fvm sub domain boundaries using macroscopic variables and their derivatives the new method is validated by solving three benchmark problems i e two and three dimensional lid driven cavity flows and two dimensional unsteady flow around a squared section cylinder these problems are analyzed with pure fvm pure lbm and finite type lb hybrid method ftlbhm and the related results are compared with each other and with benchmark data these comparisons clearly demonstrate the accuracy of the present novel methodology for simulating steady unsteady flow fields in two and three dimensions new lifting relations are developed to construct a hybrid ns fvm lbm mrt method hybrid method is implemented to a 3 dimensional problem for the first time based on authors knowledge using new hybrid fvm lbm mrt method to simulate flow around complex geometries such as porous media

estimating regression parameters using linear rank tests for censored data a class of estimates for regression parameters in a linear model with right censored data is proposed these estimates are derived by using linear rank tests for right censored data as estimating equations they are shown to be consistent and asymptotically normal with covariance matrix for which estimates are proposed efficient estimates within this class are derived together with conditions when they are fully efficient 0 introduction the problem of estimating regression coefficients using ranks has received much attention in the literature for uncensored data and the results are summarized very nicely in hettmansperger 1984 there has also been some work with censored data for the two sample problem specifically louis 1981 considered estimation of a scale change between two distributions with the use of a log rank test whereas wei and gail 1983 generalize to other linear rank tests as well we shall consider the general problem of multiple linear regression and show how linear rank tests can be used as estimating equations the resulting linear rank estimates will be shown to be asymptotically normal with variances that can be estimated consistently we shall also look at the efficiency of these estimates under various situations

phase plane for narrow band random excitation of a duffing oscillator abstract the response of a duffing oscillator to narrow band random excitation is consedered it is shown that multiple values of the response can occur at some frequencies for very narrow excitation bandwidths but these reduce to a single value as the bandwidth is increased time histories of quantities such as mean square displacement are obtained from the time dependent fokker planck equation smoothed time histories of mean square velocity and displacement are plotted on a phase plane the phase plane can show either one sink or two sinks and a saddle point as in the sinusoidal case but again excitation bandwith determines whether or not multiple sinks occur

nonparametric analysis of an accelerated failure time model summary survival distributions can be characterized by and compared through their hazard functions tests using a proportional hazards model have good power if the two hazards do not cross but without time dependent covariates can have low power if they do the method contained herein is designed to provide a complement to the proportional hazards model differences in survival distributions are parameterized by a scale change and the log rank statistic is used to generate an estimate of the change and a confidence interval based test this approach is fully efficient for the weibull family with no censoring in general it compares favourably with the proportional hazards approach although neither method dominates

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

on least squares regression with censored data summary the semiparametric accelerated failure time model relates the logarithm of the failure time linearly to the covariates while leaving the error distribution unspecified the present paper describes simple and reliable inference procedures based on the least squares principle for this model with right censored data the proposed estimator of the vector valued regression parameter is an iterative solution to the buckley james estimating equation with a preliminary consistent estimator as the starting value the estimator is shown to be consistent and asymptotically normal a novel resampling procedure is developed for the estimation of the limiting covariance matrix extensions to marginal models for multivariate failure time data are considered the performance of the new inference procedures is assessed through simulation studies illustrations with medical studies are provided

censored regression with the multistate accelerated sojourn times model many disease processes are characterized by two or more successive health states and it is often of interest and importance to assess state specific covariate effects however with incomplete follow up data such inference has not been satisfactorily addressed in the literature we model the logarithm transformed sojourn time in each state as linearly related to the covariates however neither the distributional form of the error term nor the dependence structure of the states needs to be specified we propose a regression procedure to accommodate incomplete follow up data asymptotic theory is presented along with some tools for goodness of fit diagnostics simulation studies show that the proposal is reliable for practical use we illustrate it by application to a cancer clinical trial

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

median regression with censored cost data because of the skewness of the distribution of medical costs we consider modeling the median as well as other quantiles when establishing regression relationships to covariates in many applications the medical cost data are also right censored in this article we propose semiparametric procedures for estimating the parameters in median regression models based on weighted estimating equations when censoring is present numerical studies are conducted to show that our estimators perform well with small samples and the resulting inference is reliable in circumstances of practical importance the methods are applied to a dataset for medical costs of patients with colorectal cancer

parallel dsmc method using dynamic domain decomposition a general parallel direct simulation monte carlo method using unstructured mesh is introduced which incorporates a multi level graph partitioning technique to dynamically decompose the computational domain the current dsmc method is implemented on an unstructured mesh using particle raytracing technique which takes the advantages of the cell connectivity information in addition various strategies applying the stop at rise sar ieee trans comput 1988 39 1073 1087 scheme is studied to determine how frequent the domain should be re decomposed a high speed bottom driven cavity flow including small medium and large problems based on the number of particles and cells are simulated corresponding analysis of parallel performance is reported on ibm sp2 parallel machine up to 64 processors analysis shows that degree of imbalance among processors with dynamic load balancing is about 1 6 1 2 of that without dynamic load balancing detailed time analysis shows that degree of imbalance levels off very rapidly at a relatively low value with increasing number of processors when applying dynamic load balancing which makes the large problem size fairly scalable for processors more than 64 in general optimal frequency of activating sar scheme decreases with problem size at the end the method is applied to compute two two dimensional hypersonic flows a three dimensional hypersonic flow and a three dimensional near continuum twin jet gas flow to demonstrate its superior computational capability and compare with experimental data and previous simulation data wherever available

a combined continuum dsmc technique for multiscale analysis of microfluidic filters a multiscale method that combines continuum fluid models with the direct simulation monte carlo dsmc method is presented the continuum regions are treated by stokes equations and a scattered point based finite cloud method is employed to solve the stokes equations the continuum and dsmc regions are combined by an overlapped schwarz alternating method with dirichlet dirichlet type boundary conditions a scattered point interpolation scheme is developed to interpolate the solution between subdomains the convergence characteristics of the multiscale approach are investigated in detail specifically the dependence of convergence on the overlap size the dsmc noise and the number of time steps employed in the dsmc algorithm are studied while the convergence depends weakly on the dsmc noise and the overlap size the number of dsmc time steps simulated in each coupling iteration should be selected so that the total time steps simulated until convergence of the coupled process is close to the time constant of the dsmc subsystem steady state analysis of microfluidic filters is studied in detail using the multiscale approach the multiscale approach is also applied for the simulation of a membrane with an array of microfluidic filters and a dual stage microfluidic device with an array of microfluidic filters for particle trapping and sorting

responses of discretized systems under narrow band nonstationary random excitations part 2 nonlinear problems in a companion paper the extended stochastic central difference escd method was presented for computation of responses of linear multi degrees of freedom mdof systems under narrow band stationary and nonstationary random excitations the present paper is concerned with the generalization of the escd method for computation of nonlinear mdof systems the generalization is based on a combination of the escd method with statistical linearization sl technique modified adaptive time scheme ats and time coordinate transformation tct unlike the conventional sl technique the generalized escd method is applicable to mdof systems with large nonlinearities comparison is made of the computed results applying the generalized escd method to those obtained by the monte carlo simulation mcs excellent agreements were obtained it was observed that the proposed method is very efficient compared with the mcs

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

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

first passage time of nonlinear ship rolling in narrow band non stationary random seas the generalized extended stochastic central difference gescd method is applied to study the response statistics and first passage time of nonlinear ship rolling in narrow band stationary and non stationary random seas the gescd method is based on a combination of the extended stochastic central difference method with a statistical linearization technique modified adaptive time scheme and time coordinate transformation the extended stochastic central difference method is however an extension of the stochastic central difference method for the determination of the recursive mean square or covariance of responses of systems under narrow band stationary and non stationary random disturbances approximate first passage probabilities of nonlinear systems based on the modified mean rate of various crossings proposed earlier by the first author were determined it is concluded that the gescd method is very accurate simple and efficient to apply compared with monte carlo simulation the proposed method is applicable to cases with large nonlinearities and intensive random excitations the approximate first passage probabilities of the nonlinear system determined by the proposed approach are very accurate as they are in excellent agreement with those evaluated by the monte carlo simulation it is believed that the model considered in this paper is a closer representation to reality than those reported earlier in the literature