| 000 | 00000nam u2200205 ac4500 | |
| 001 | 000046020616 | |
| 005 | 20200311163706 | |
| 008 | 200311s2018 flu b 001 0 eng d | |
| 010 | ▼a 2018006955 | |
| 020 | ▼a 9781466592650 (hardback : alk. paper) | |
| 035 | ▼a (KERIS)REF000018642951 | |
| 040 | ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a HB849.47 ▼b .F82 2018 |
| 082 | 0 0 | ▼a 001.4/22 ▼2 23 |
| 084 | ▼a 001.422 ▼2 DDCK | |
| 090 | ▼a 001.422 ▼b F949p | |
| 100 | 1 | ▼a Fu, Wenjiang. |
| 245 | 1 2 | ▼a A practical guide to age-period-cohort analysis : ▼b the identification problem and beyond / ▼c Wenjiang Fu. |
| 260 | ▼a Boca Raton : ▼b CRC Press, Taylor & Francis Group, ▼c c2018. | |
| 300 | ▼a xx, 230 p. ; ▼c 25 cm. | |
| 504 | ▼a Includes bibliographical references (p.219-225) and index. | |
| 650 | 0 | ▼a Cohort analysis. |
| 650 | 0 | ▼a Age groups ▼x Statistical methods. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 001.422 F949p | 등록번호 111825916 (1회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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.
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.
A popular and efficient tool to analyze grouped data of a certain event, APC analysis is used in a range of application areas, including public health, social science, demography, and economics. This book first describes the basic graphic presentation and modeling of APC data using R. Taking a more theoretical point of view, it then presents various methods for addressing the complex identification problem. Appendices cover the necessary background material.
정보제공 :
목차
1. Motivation of APC Analysis 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 APC Data | Graphic Methods D Plots in Age, Period, and Cohort D Plots in Age, Period, and Cohort Suggested Readings Exercises 3. Preliminary Analysis of APC 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 Modeling Over-dispersion with Quasi-likelihood R Programming for Loglinear Models Suggested Readings Exercises 4. APC Models | Complexity with Linearly Dependent Co-variates Lexis Diagram and Patterns 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 Models Regression with Linearly Dependent Covariates Age-Period-Cohort Models and Complexity R Programming for Generating the Design Matrix for APC Models Suggested Readings Exercises 5. APC Models | The Identi_cation Problem and 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. Intrinsic Estimator, the Rationale and Properties Structure of Multiple Estimators Intrinsic Estimator: Unbiased Estimates and Other Properties Robust Estimation via Sensitivity Analysis Summary of Asymptotic Properties of the Multiple Estimators Computation of Intrinsic Estimator and Standard Errors Computation of Intrinsic Estimator Computation of Standard Errors Suggested Readings Exercises 7. Data Analysis with Intrinsic Estimator and Comparison 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 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 Estimability of Intrinsic Estimator 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 Co-hort 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 Suggested Readings Exercises 10. Unequal Spans in Age and Period Groups APC Data with Unequal Spans The Intend-to-Collapse (ITC) Method APC Models for Unequal Spans Identi_cation Problem and Intrinsic Estimator for Unequal Span Data Multiple Estimators and Identi_cation Problem The Intrinsic Estimator for Unequal Span Data Analysis of Unequal Span Data Fitting Unequal Span Data with R Function apclinkfit Exercises Bibliography Index