Regression Modeling Strategies (Hardcover)

Description 

There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.

 

Table of Contents

Introduction * General Aspects of Fitting Regression Models * Missing Data * Multivariable Modeling Strategies * Resampling, Validating, Describing, and Simplifying the Model * S-PLUS Software * Case Study in Least Squares Fitting and Interpretation of a Linear Model * Case Study in Imputation and Data Reduction * Overview of Maximum Likelihood Estimation * Binary Logistic Regression * Logistic Model Case Study 1: Predicting Cause of Death * Logistic Model Case Study 2: Survival of Titanic Passengers * Ordinal Logistic Regression * Case Study in Ordinal Regrssion, Data Reduction, and Penalization * Models Using Nonparametic Transformations of X and Y * Introduction to Survival Analysis * Parametric Survival Models * Case Study in Parametric Survival Modeling and Model Approximation * Cox Proportional Hazards Regression Model * Case Study in Cox Regression

描述

有許多書籍是關於個別統計工具（生存模型、一般線性模型等）的優秀知識來源，但是數據分析的藝術在於選擇和使用多種工具。用 Chatfield 的話來說，“...學生通常知道回歸的技術細節，但不一定知道何時以及如何應用它。這就需要文獻和統計教學在技術和問題解決策略之間取得更好的平衡。”無論是分析風險因素、調整觀察研究中的偏差，還是開發預測模型，都存在一些常見問題，但很少有回歸分析的文本涉及這些問題。例如，大多數數據集（除了教科書中使用的數據集）中都存在缺失數據，但大多數回歸分析的文本並未包含有效處理此類數據的方法，而缺失數據的文本也未涵蓋回歸建模。

目錄

引言 * 回歸模型擬合的一般方面 * 缺失數據 * 多變量建模策略 * 重抽樣、驗證、描述和簡化模型 * S-PLUS 軟體 * 最小二乘擬合和線性模型解釋的案例研究 * 插補和數據降維的案例研究 * 最大概似估計概述 * 二元羅吉斯回歸 * 羅吉斯模型案例研究1：預測死因 * 羅吉斯模型案例研究2：泰坦尼克號乘客的生存 * 有序羅吉斯回歸 * 有序回歸、數據降維和懲罰的案例研究 * 使用 X 和 Y 的非參數轉換的模型 * 生存分析簡介 * 參數生存模型 * 參數生存建模和模型逼近的案例研究 * Cox 比例風險回歸模型 * Cox 回歸的案例研究