Stata最大似然估计，第5版

English | 2024 | ISBN: 159718411X | 472 pages | True EPUB | 12.71 MB

Maximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata's command for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation. The fifth edition includes a new second chapter that demonstrates the easy-to-usemlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming. The core of the book focuses on Stata'sml command. It shows you how to take full advantage ofml's noteworthy features - Linear constraints - Four optimization algorithms (Newton–Raphson, DFP, BFGS, and BHHH) - Observed information matrix (OIM) variance estimator - Outer product of gradients (OPG) variance estimator - Huber/White/sandwich robust variance estimator - Cluster–robust variance estimator - Complete and automatic support for survey data analysis - Direct support of evaluator functions written in Mata When appropriate options are used, many of these features are provided automatically byml and require no special programming or intervention by the researcher writing the estimator. In later chapters, you will learn how to take advantage of Mata, Stata's matrix programming language. For ease of programming and potential speed improvements, you can write your likelihood-evaluator program in Mata and continue to usemlto control the maximization process. A new chapter in the fifth edition shows how you can use themoptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata. In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model, a model that is not available otherwise in Stata. The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book, users will be able to write their own estimation commands—commands that look and behave just like the official estimation commands in Stata. Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use, you need this book.

英文| 2024 |国际标准图书编号：159718411X| 472页|真正的EPUB | 12.71 MB Stata的最大似然估计，第五版是所有希望在Stata中编写最大似然（ML）估计器的学科研究人员的重要参考和指南。除了全面介绍Stata编写ML估计器的命令外，这本书还概述了最大似然的基础以及如何思考ML估计。第五版新增了第二章，演示了易于使用的mlexp命令。此命令允许您直接指定似然函数并执行估计，而无需任何编程。这本书的核心是Stata'sml命令。它向您展示了如何充分利用ml的值得注意的特征-线性约束-四种优化算法（Newton–Raphson、DFP、BFGS和BHHH）-观测信息矩阵（OIM）方差估计器-梯度外积（OPG）方差估计器-Huber/White/三明治稳健方差估计器-聚类——稳健方差估计器——对调查数据分析的完全和自动支持-直接支持用Mata编写的评估器函数。当使用适当的选项时，许多这些特征由ml自动提供，不需要编写估计器的研究人员进行特殊的编程或干预。在后面的章节中，您将学习如何利用Stata的矩阵编程语言Mata。为了便于编程和潜在的速度改进，您可以用Mata编写似然评估器程序，并继续使用ml来控制最大化过程。第五版中的一个新章节展示了如果你想完全在Mata中实现你的最大似然估计器，如何使用Mata函数的themoptimize（）套件。在最后一章中，作者说明了从对数似然函数到完全可操作的估计命令所需的主要步骤。这是使用几个不同的模型完成的：logit和probit、线性回归、Weibull回归、Cox比例风险模型、随机效应回归和看似无关的回归。本版新增了一个双变量泊松模型的示例，该模型在Stata中是不可用的。作者为开发自己的估算命令提供了广泛的建议。在本书的帮助下，用户将能够编写自己的估算命令——这些命令的外观和行为与Stata中的官方估算命令一样。无论你是想为自己的研究找到一个特殊的ML估计器，还是想编写一个通用的ML估计器供他人使用，你都需要这本书。

---

Download from free file storage

 Download from nitroflare.com  Download from uploadgig.com

---

我要反馈

---

本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。