Ebook: Logistic Regression with Missing Values in the Covariates

In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications.
The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.

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In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications.
The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.

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In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications.
The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.
Content:
Front Matter....Pages i-x
Introduction....Pages 1-3
The Complete Data Case....Pages 4-5
Missing Value Mechanisms....Pages 6-7
Estimation Methods....Pages 8-25
Quantitative Comparisons: Asymptotic Results....Pages 26-60
Quantitative Comparisons: Results of Finite Sample Size Simulation Studies....Pages 61-72
Examples....Pages 73-79
Sensitivity Analysis....Pages 80-84
General Regression Models with Missing Values in One of Two Covariates....Pages 85-94
Generalizations for More Than two Covariates....Pages 95-97
Missing Values and Subsampling....Pages 98-102
Further Examples....Pages 103-111
Discussion....Pages 112-115
Back Matter....Pages 116-143

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In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications.
The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.
Content:
Front Matter....Pages i-x
Introduction....Pages 1-3
The Complete Data Case....Pages 4-5
Missing Value Mechanisms....Pages 6-7
Estimation Methods....Pages 8-25
Quantitative Comparisons: Asymptotic Results....Pages 26-60
Quantitative Comparisons: Results of Finite Sample Size Simulation Studies....Pages 61-72
Examples....Pages 73-79
Sensitivity Analysis....Pages 80-84
General Regression Models with Missing Values in One of Two Covariates....Pages 85-94
Generalizations for More Than two Covariates....Pages 95-97
Missing Values and Subsampling....Pages 98-102
Further Examples....Pages 103-111
Discussion....Pages 112-115
Back Matter....Pages 116-143
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