Practical Guide to Age-Period-Cohort Analysis

Regular price €84.99
A01=Wenjiang Fu
Age Cohort Model
Age Effect Estimates
Age Group_Uncategorized
Age Group_Uncategorized
age-period-cohort
APC Analysis
APC Model
Asymptotic Studies
Author_Wenjiang Fu
automatic-update
Category1=Non-Fiction
Category=JHBC
Category=JMB
Category=PBT
Category=UFM
Cohort Effect Estimates
Cohort Effects
Cohort Trend
COP=United States
Data Set
Delivery_Delivery within 10-20 working days
eq_computing
eq_isMigrated=2
eq_non-fiction
eq_society-politics
Estimable Function
HIV Mortality
Intensive Simulation Studies
Intrinsic Estimator
Language_English
Loglinear Model
Lung Cancer Mortality
Lung Cancer Mortality Data
PA=Available
Parameter Identification Problem
PCA Approach
Period Effect Model
Price_€50 to €100
Profile Log Likelihood
PS=Active
Quasi-likelihood Approach
Ridge Estimator
softlaunch
True Age Effect
True Parameter Values
Unequal Spans

Product details

  • ISBN 9781466592650
  • Weight: 521g
  • Dimensions: 156 x 234mm
  • Publication Date: 25 Apr 2018
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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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.

    Wenjiang Fu