Simulated Maximum Likelihood Estimation Of Discrete Models With Group Data
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Simulated Maximum Likelihood Estimation Of Discrete Models With Group Data
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Author : Lung-Fei Lee
language : en
Publisher:
Release Date : 1993
Simulated Maximum Likelihood Estimation Of Discrete Models With Group Data written by Lung-Fei Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Estimation theory categories.
Simulated Maximum Likelihood Estimation Of Dynamic Discrete Choice Statistical Models
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Author : Lung-Fei Lee
language : en
Publisher:
Release Date : 1994
Simulated Maximum Likelihood Estimation Of Dynamic Discrete Choice Statistical Models written by Lung-Fei Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Monte Carlo method categories.
Robust Inference And Group Sequential Methods In Discrete Hazard Models
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Author : Vinh Quang Nguyen
language : en
Publisher:
Release Date : 2011
Robust Inference And Group Sequential Methods In Discrete Hazard Models written by Vinh Quang Nguyen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
The current research focuses on the analysis of discrete-time data arising from periodic follow-up using discrete-time hazard models (analogs to the Cox proportional hazards model) when the model is misspecified. We begin by providing scientific examples that motivate the present research and provide some background and notation that lays the foundation for the remainder of the dissertation. We then describe methods for analyzing grouped proportional hazards data, and present simulation results to convey their relative performances. Focusing on discrete hazard models for analyzing grouped survival data, we then explore the impact of model misspecification, namely a time-varying treatment effect, on the maximum likelihood (ML) estimator of commonly used discrete-time models in the two-sample setting (e.g., clinical trials). We show that the ML estimator is consistent to a quantity that depends on the censoring pattern of the observations and the maximum follow-up time of the study. We propose a censoring-robust estimator that removes the influence of censoring by re-weighing observations based on the inverse of the Kaplan-Meier estimator of the censoring times for each group and derive its asymptotic distribution. Simulation is used to compare the two estimators in different scenarios and the proposed estimator is applied to data from clinical trial in HIV/AIDS. Next, we describe how robust inference can be extended to the observational study setting where multiple (possibly continuous) covariates are involved. In this setting, we rely on survival trees to identify group-specific censoring to aid in the estimation of the censoring distribution. Finally, we explore the use of the censoring-robust estimator in an interim testing context that is typical of late stage clinical trials. To that end, we derive the joint asymptotic distribution of the censoring-robust estimator calculated over time. We note that the estimating equation of the censoring-robust estimator lacks an independent increments structure, rendering standard group sequential methods inapplicable. We then propose a strategy for designing and evaluating group sequential trials based on the censoring-robust estimator using existing pilot data.
Discrete Choice Methods With Simulation
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Author : Kenneth Train
language : en
Publisher: Cambridge University Press
Release Date : 2009-07-06
Discrete Choice Methods With Simulation written by Kenneth Train and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-06 with Business & Economics categories.
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
From Data To Model
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Author : Jan C. Willems
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
From Data To Model written by Jan C. Willems and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
The problem of obtaining dynamical models directly from an observed time-series occurs in many fields of application. There are a number of possible approaches to this problem. In this volume a number of such points of view are exposed: the statistical time series approach, a theory of guaranted performance, and finally a deterministic approximation approach. This volume is an out-growth of a number of get-togethers sponsered by the Systems and Decision Sciences group of the International Institute of Applied Systems Analysis (IIASA) in Laxenburg, Austria. The hospitality and support of this organization is gratefully acknowledged. Jan Willems Groningen, the Netherlands May 1989 TABLE OF CONTENTS Linear System Identification- A Survey page 1 M. Deistler A Tutorial on Hankel-Norm Approximation 26 K. Glover A Deterministic Approach to Approximate Modelling 49 C. Heij and J. C. Willems Identification - a Theory of Guaranteed Estimates 135 A. B. Kurzhanski Statistical Aspects of Model Selection 215 R. Shibata Index 241 Addresses of Authors 246 LINEAR SYSTEM IDENTIFICATION· A SURVEY M. DEISTLER Abstract In this paper we give an introductory survey on the theory of identification of (in general MIMO) linear systems from (discrete) time series data. The main parts are: Structure theory for linear systems, asymptotic properties of maximum likelihood type estimators, estimation of the dynamic specification by methods based on information criteria and finally, extensions and alternative approaches such as identification of unstable systems and errors-in-variables. Keywords Linear systems, parametrization, maximum likelihood estimation, information criteria, errors-in-variables.
The Solution And Estimation Of Discrete Choice Dynamic Programming Models By Simulation And Interpolation
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Author : Michael P. Keane
language : en
Publisher:
Release Date : 1994
The Solution And Estimation Of Discrete Choice Dynamic Programming Models By Simulation And Interpolation written by Michael P. Keane and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Dynamic programming categories.
Maximum Likelihood Estimation And Inference
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Author : Russell B. Millar
language : en
Publisher: John Wiley & Sons
Release Date : 2011-07-26
Maximum Likelihood Estimation And Inference written by Russell B. Millar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-26 with Mathematics categories.
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling. Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. Provides all program code and software extensions on a supporting website. Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.
Exact Maximum Likelihood Estimation Of Observation Driven Econometric Models
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Author : Francis X. Diebold
language : en
Publisher:
Release Date : 1996
Exact Maximum Likelihood Estimation Of Observation Driven Econometric Models written by Francis X. Diebold and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Econometric models categories.
The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.
Applications Of Simulation Methods In Environmental And Resource Economics
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Author : Riccardo Scarpa
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-08-12
Applications Of Simulation Methods In Environmental And Resource Economics written by Riccardo Scarpa and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-12 with Business & Economics categories.
Simulation methods are revolutionizing the practice of applied economic analysis. In this book, leading researchers from around the world discuss interpretation issues, similarities and differences across alternative models, and propose practical solutions for the choice of the model and programming. Case studies show the practical use and the results brought forth by the different methods.
Discrete Choice Methods With Simulation
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Author : Kenneth E. Train
language : en
Publisher: Cambridge University Press
Release Date : 2009-06-30
Discrete Choice Methods With Simulation written by Kenneth E. Train and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-30 with Business & Economics categories.
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. This second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.