A practical guide to age-period-cohort analysis : the identification problem and beyond

著者

• Fu, Wenjiang

書誌事項

A practical guide to age-period-cohort analysis : the identification problem and beyond

Wenjiang Fu

（A Chapman & Hall book）

CRC Press, c2018

• : hardback

注記

Includes bibliographical references (p. 219-225) and index

内容説明・目次

内容説明

Age-Period-Cohort analysis has a wide range of applications, from chronic disease incidence and mortality data in public health and epidemiology, to many social events (birth, death, marriage, etc) in social sciences and demography, and most recently investment, healthcare and pension contribution in economics and finance. Although APC analysis has been studied for the past 40 years and a lot of methods have been developed, the identification problem has been a major hurdle in analyzing APC data, where the regression model has multiple estimators, leading to indetermination of parameters and temporal trends. A Practical Guide to Age-Period Cohort Analysis: The Identification Problem and Beyond provides practitioners a guide to using APC models as well as offers graduate students and researchers an overview of the current methods for APC analysis while clarifying the confusion of the identification problem by explaining why some methods address the problem well while others do not. Features * Gives a comprehensive and in-depth review of models and methods in APC analysis. * Provides an in-depth explanation of the identification problem and statistical approaches to addressing the problem and clarifying the confusion. * Utilizes real data sets to illustrate different data issues that have not been addressed in the literature, including unequal intervals in age and period groups, etc. Contains step-by-step modeling instruction and R programs to demonstrate how to conduct APC analysis and how to conduct prediction for the future Reflects the most recent development in APC modeling and analysis including the intrinsic estimator Wenjiang Fu is a professor of statistics at the University of Houston. Professor Fu's research interests include modeling big data, applied statistics research in health and human genome studies, and analysis of complex economic and social science data.

目次

1. Motivation of Age - Period - Cohort Analysis Examples and Applications What Is Age-Period-Cohort Analysis? Why Age - Period - Cohort Analysis? Four Data Sets in APC Studies Special Features of These Data Sets Data Source R Programming and Video Online Instruction Suggested Readings Exercises 2. Preliminary Analysis of Age - Period - Cohort Data - Graphic Methods D Plots in Age, Period, and Cohort D Plot in Age, Period, and Cohort Suggested Readings Exercises 3. Preliminary Analysis of Age - Period - Cohort Data - Basic Models Linear Models for Continuous Response Single Factor Models Two Factor Models R Programming for Linear Models Loglinear Models for Discrete Response Single Factor Models Two Factor Models R Programming for Loglinear Models Suggested Readings Exercises 4. Age-Period-Cohort Model - Complexity with Linearly Dependent Covariates Lexis Diagram and Pattern in Age, Period, and Cohort Lexis Diagram and Dependence among Age, Period, and Cohort Explicit Pattern in APC Data with Identical Spans in Age and Period Implicit Pattern in APC Data with Unequal Spans in Age and Period Complexity in Full Age - Period - Cohort Model Regression with Linearly Dependent Covariates Age-Period-Cohort Models and the Complexity R Programming for Generating the Design Matrix for APC Models Suggested Readings Exercises 5. Age-Period-Cohort Model - The Identification Problem and Various Approaches The Identification Problem and Confusion Two Popular Approaches to the Identification Problem Constraint Approach Estimable Function Approach Other Approaches to the Identification Problem Suggested Readings Exercises 6. The Intrinsic Estimator, the Rationale and Properties Structure of Multiple Estimators of Age-Period-Cohort Models Intrinsic Estimator - Unbiased Estimation and Other Properties Robust Estimation via Sensitivity Analysis Summary of Asymptotic Properties of the Multiple Estimators Computation of the Intrinsic Estimator and Standard Errors Computation of the Intrinsic Estimator Computation of the Standard Errors Suggested Readings Exercises 7. Data Analysis with Intrinsic Estimator and Comparison with Others Illustration of Data Analysis with the Intrinsic Estimator Modeling Lung Cancer Mortality Data among US Males Intrinsic Estimator of Linear Models Intrinsic Estimator of Loglinear Models Modeling the HIV Mortality Data Intrinsic Estimator of Linear Models Intrinsic Estimator of Loglinear Models Illustration of Data Analysis with Constrained Estimators Illustration of Equality Constraints Illustration of Non-contrast Constraints Suggested Readings Exercises 8. Asymptotic Behavior of the Multiple Estimators - Theoretical Results Settings and Strategies to Study the Asymptotics of Multiple Estimators Assumptions and Regularity Conditions for the Asymptotics Asymptotics of Multiple Estimators Asymptotics of Multiple Estimators with Fixed t Asymptotics of Linearly Constrained Estimators Linear constraint on age effects Linear constraint on period or cohort effects Suggested Readings Exercises 9. Variance Estimation and Selection of Side-condition Variance Estimation of the Intrinsic Estimator The Delta Method for the Variance of Period and Cohort Effect Estimates Comparison of Standard Errors between the PCA and Delta Methods Selection of Side Condition Side-conditions for One-way ANOVA Models Side-conditions for Two-way ANOVA Models Side-conditions for Age - Period - Cohort Models Conclusion on Side-condition Selection 10. Unequal Spans in Age Groups and Periods with Applications to Survey Data APC Data with Unequal Spans The Intend-to-Collapse (ITC) Method APC Models for Unequal Spans Identification Problem and Intrinsic Estimator for Unequal Span Data Multiple Estimators and Identification Problem The Intrinsic Estimator for Unequal Span Data Analyzing APC Data with Unequal Spans by the Intrinsic Estimator Fitting Unequal Span Data with R Function apclinkfit Exercises Bibliography

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BB27839274
• ISBN:
  • 9781466592650
• LCCN: 2018006955
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Boca Raton, Fla.
• ページ数/冊数: xx, 230 p.
• 大きさ: 25 cm
• 分類:
  • LCC : HB849.47
  • DC23 : 001.4/22
• 件名:
  • LCSH : Cohort analysis
  • LCSH : Age groups -- Statistical methods
• 親書誌ID:
  • BB04603869