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Maximum Likelihood Estimation With Stata


Maximum Likelihood Estimation With Stata
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Maximum Likelihood Estimation With Stata Fourth Edition


Maximum Likelihood Estimation With Stata Fourth Edition
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Author : William Gould
language : en
Publisher: Stata Press
Release Date : 2010-10-27

Maximum Likelihood Estimation With Stata Fourth Edition written by William Gould and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-27 with Mathematics categories.


Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.



Maximum Likelihood Estimation With Stata


Maximum Likelihood Estimation With Stata
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Author : William Gould
language : en
Publisher:
Release Date : 2003

Maximum Likelihood Estimation With Stata written by William Gould and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Mathematics categories.




Maximum Likelihood Estimation With Stata Third Edition


Maximum Likelihood Estimation With Stata Third Edition
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Author : William Gould
language : en
Publisher: Stata Press
Release Date : 2006

Maximum Likelihood Estimation With Stata Third Edition written by William Gould and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computers categories.


Written by the creators of Stata's likelihood maximization features, Maximum Likelihood Estimation with Stata, Third Edition continues the pioneering work of the previous editions. Emphasizing practical implications for applied work, the first chapter provides an overview of maximum likelihood estimation theory and numerical optimization methods. With step-by-step instructions, the next several chapters detail the use of Stata to maximize user-written likelihood functions. Various examples include logit, probit, linear, Weibull, and random-effects linear regression as well as the Cox proportional hazards model. The final chapters describe how to add a new estimation command to Stata. Assuming a familiarity with Stata, this reference is ideal for researchers who need to maximize their own likelihood functions. New ml commands and their functions: constraint: fits a model with linear constraints on the coefficient by defining your constraints; accepts a constraint matrix ml model: picks up survey characteristics; accepts the subpop option for analyzing survey data optimization algorithms: Berndt-Hall-Hall-Hausman (BHHH), Davidon-Fletcher-Powell (DFP), Broyden-Fletcher-Goldfarb-Shanno (BFGS) ml: switches between optimization algorithms; computes variance estimates using the outer product of gradients (OPG)



Maximum Likelihood Estimation With Stata


Maximum Likelihood Estimation With Stata
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Author : Jeffrey S. Pitblado
language : en
Publisher:
Release Date : 2024

Maximum Likelihood Estimation With Stata written by Jeffrey S. Pitblado and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Social sciences categories.




Applied Panel Data Analysis For Economic And Social Surveys


Applied Panel Data Analysis For Economic And Social Surveys
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Author : Hans-Jürgen Andreß
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-01-24

Applied Panel Data Analysis For Economic And Social Surveys written by Hans-Jürgen Andreß 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 2013-01-24 with Social Science categories.


Many economic and social surveys are designed as panel studies, which provide important data for describing social changes and testing causal relations between social phenomena. This textbook shows how to manage, describe, and model these kinds of data. It presents models for continuous and categorical dependent variables, focusing either on the level of these variables at different points in time or on their change over time. It covers fixed and random effects models, models for change scores and event history models. All statistical methods are explained in an application-centered style using research examples from scholarly journals, which can be replicated by the reader through data provided on the accompanying website. As all models are compared to each other, it provides valuable assistance with choosing the right model in applied research. The textbook is directed at master and doctoral students as well as applied researchers in the social sciences, psychology, business administration and economics. Readers should be familiar with linear regression and have a good understanding of ordinary least squares estimation. ​



Modeling Ordered Choices


Modeling Ordered Choices
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Author : William H. Greene
language : en
Publisher: Cambridge University Press
Release Date : 2010-04-08

Modeling Ordered Choices written by William H. Greene 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 2010-04-08 with Business & Economics categories.


It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.



Handbook Of Statistical Analyses Using Stata


Handbook Of Statistical Analyses Using Stata
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Author : Brian S. Everitt
language : en
Publisher: CRC Press
Release Date : 2006-11-15

Handbook Of Statistical Analyses Using Stata written by Brian S. Everitt and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-15 with Mathematics categories.


With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Fulfilling this need, AHandbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. This edition covers many



Discovering Structural Equation Modeling Using Stata


Discovering Structural Equation Modeling Using Stata
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Author : Alan C. Acock
language : en
Publisher: Stata Press
Release Date : 2013-04-01

Discovering Structural Equation Modeling Using Stata written by Alan C. Acock and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-01 with Mathematics categories.


Discovering Structural Equation Modeling Using Stata is devoted to Stata’s sem command and all it can do. You’ll learn about its capabilities in the context of confirmatory factor analysis, path analysis, structural equation modeling, longitudinal models, and multiple-group analysis. The book describes each model along with the necessary Stata code, which is parsimonious, powerful, and can be modified to fit a wide variety of models. Downloadable data sets enable you to run the programs and learn in a hands-on way. A particularly exciting feature of Stata is the SEM Builder. This graphic interface for structural equation modeling allows you to draw publication-quality path diagrams and fit the models without writing any programming code. When you fit a model with the SEM Builder, Stata automatically generates the complete code that you can save for future use. Use of this unique tool is extensively covered in an appendix, and brief examples appear throughout the text. Requiring minimal background in multiple regression, this practical reference is designed primarily for those new to structural equation modeling. Some experience with Stata would be helpful but is not essential. Readers already familiar with structural equation modeling will also find the book’s State code useful.



Regression Models For Categorical Dependent Variables Using Stata Second Edition


Regression Models For Categorical Dependent Variables Using Stata Second Edition
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Author : J. Scott Long
language : en
Publisher: Stata Press
Release Date : 2006

Regression Models For Categorical Dependent Variables Using Stata Second Edition written by J. Scott Long and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computers categories.


The goal of the book is to make easier to carry out the computations necessary for the full interpretation of regression nonlinear models for categorical outcomes usign Stata.



Regression Models For Categorical And Limited Dependent Variables


Regression Models For Categorical And Limited Dependent Variables
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Author : J. Scott Long
language : en
Publisher: SAGE
Release Date : 1997-01-09

Regression Models For Categorical And Limited Dependent Variables written by J. Scott Long and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-01-09 with Mathematics categories.


THE APPROACH "J. Scott Long′s approach is one that I highly commend. There is a decided emphasis on the application and interpretation of the specific statistical techniques. Long works from the premise that the major difficulty with the analysis of limited and categorical dependent variables (LCDVs) is the complexity of interpreting nonlinear models, and he provides tools for interpretation that can be widely applied across the different techniques." --Robert L. Kaufman, Sociology, Ohio State University "A thorough and comprehensive introduction to analyzing categorical and limited dependent variables from a traditional regression perspective that provides unusually clear discussions concerning estimation, identification, and the multiplicity of models available to the researcher to analyze such data." --Scott Hershberger, Psychology, University of Kansas THE ORGANIZATION "The thing that impresses me the most about this book is how organized it is. The chapters are in excellent logical sequence. There is a useful repetition of important concepts (e.g., estimation, hypothesis testing) from chapter to chapter. J. Scott Long has done a terrific job of organizing like things from disparate literatures, such as the scaler measures of fit in Chapter 4." --Herbert L. Smith, Sociology, University of Pennsylvania "A major strength of the book is the way that it is organized. The chapter about each technique is written in a highly organized and parallel format. First the statistical basis and assumptions for the particular model are developed, then estimation issues are considered, then issues of testing and interpretation are considered, then variations and extensions are explored." --Robert L. Kaufman, Sociology, Ohio State University FOR THE COURSE "I have been teaching a course on categorical data analysis to sociology graduate students for close to 20 years, but I have never found a book with which I was happy. J. Scott Long′s book, on the other hand, is nearly ideal for my objectives and preferences, and I expect that many other social scientists will feel the same way. I will definitely adopt it the next time I teach the course. It deals with the right topics in the most desirable sequence and it is clearly written." --Paul D. Allison, Sociology, University of Pennsylvania Class-tested at two major universities and written by an award-winning teacher, J. Scott Long′s book gives readers unified treatment of the most useful models for categorical and limited dependent variables (CLDVs). Throughout the book, the links among models are made explicit, and common methods of derivation, interpretation, and testing are applied. In addition, Long explains how models relate to linear regression models whenever possible. In order for the reader to see how these models can be applied, Long illustrates each model with data from a variety of applications, ranging from attitudes toward working mothers to scientific productivity. The book begins with a review of the linear regression model and an introduction to maximum likelihood estimation. It then covers the logit and probit models for binary outcomes--providing details on each of the ways in which these models can be interpreted, reviews standard statistical tests associated with maximum likelihood estimation, and considers a variety of measures for assessing the fit of a model. Long extends the binary logit and probit models to ordered outcomes, presents the multinomial and conditioned logit models for nominal outcomes, and considers models with censored and truncated dependent variables with a focus on the tobit model. He also describes models for sample selection bias and presents models for count outcomes by beginning with the Poisson regression model and showing how this model leads to the negative binomial model and zero inflated count models. He concludes by comparing and contrasting the models from earlier chapters and discussing the links between these models and models not discussed in the book, such as loglinear and event history models. Helpful exercises are included in the book with brief answers included in the appendix so that readers can practice the techniques as they read about them.