1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.
PART ONE: PRELIMINARIES Statistics and Social Science What Is Regression Analysis? Examining Data Transforming Data PART TWO: LINEAR MODELS AND LEAST SQUARES Linear Least-Square...
Preface. 0. The Applied Setting. 1. The Counting Process and Martingale Framework. 2. Local Square Integrable Martingales. 3. Finite Sample Moments and Large Sample Consistency ...
Preface 1. Introduction Part I. Preliminaries: 2. Basic Concepts 3. Basic Analysis and Optimality Conditions 4. Basic Linear Algebra 5. Krylov Subspace Methods Part II. Trust-Re...
Abstract Regression of the experimental data of one independent variable, y vs . a linear combination of functions of an independent variable of the form y = Σβ j f j (x) is con...
Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to descri...
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