
000 | 00000cam u2200205 a 4500 | |
001 | 000045820879 | |
005 | 20150127140530 | |
008 | 150126s2015 flua b 001 0 eng d | |
020 | ▼a 9781466551534 ▼q (hardback) | |
020 | ▼a 1466551534 ▼q (hardback) | |
035 | ▼a (KERIS)REF000017627631 | |
040 | ▼a CRCPR ▼b eng ▼c CRCPR ▼d OCLCO ▼d FDA ▼d OCLCQ ▼e rda ▼d 211009 | |
050 | 1 4 | ▼a HB849.47 ▼b .O27 2015 |
082 | 0 4 | ▼a 001.42 ▼2 23 |
084 | ▼a 001.42 ▼2 DDCK | |
090 | ▼a 001.42 ▼b O13a | |
100 | 1 | ▼a O'Brien, Robert M. |
245 | 1 0 | ▼a Age-period-cohort models : ▼b approaches and analyses with aggregate data / ▼c Robert M. O'Brien. |
260 | ▼a Boca Raton : ▼b CRC Press, ▼c 2015. | |
300 | ▼a xi, 204 p. : ▼b ill. ; ▼c 24 cm. | |
490 | 1 | ▼a Chapman & Hall/CRC statistics in the social and behavioral sciences series |
504 | ▼a Includes bibliographical references and index. | |
505 | 0 0 | ▼g 1. ▼t Introduction to the age, period, and cohort mix -- ▼g 2. ▼t Multiple classification models and constrained regression -- ▼g 3. ▼t Geometry of age-period-cohort (APC) models and constrained estimation -- ▼g 4. ▼t Estimable functions approach -- ▼g 5. ▼t Partitioning the variance in age-period-cohort (APC) models -- ▼g 6. ▼t Factor-characteristic approach -- ▼g 7. Conclusions : ▼t An empirical example. |
650 | 0 | ▼a Cohort analysis. |
650 | 0 | ▼a Age groups ▼x Statistical methods. |
740 | 0 2 | ▼a Empirical example. |
830 | 0 | ▼a Statistics in the social and behavioral sciences series. |
945 | ▼a KLPA |
소장정보
No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
---|---|---|---|---|---|---|---|
No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 001.42 O13a | 등록번호 111729717 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
목차
Introduction to the Age, Period, and Cohort Mix
Introduction
Interest in Age, Period, and Cohort
Importance of Cohorts
Plan for the BookMultiple Classification Models and Constrained Regression
Introduction
Linearly Coded Age?Period?Cohort (APC) Model
Categorically Coded APC Model
Generalized Linear Models
Null Vector
Model Fit
Solution Is Orthogonal to the Constraint
Examining the Relationship between Solutions
Differences between Constrained Solutions as Rotations of Solutions
Solutions Ignoring One or More of the Age, Period, or Cohort Factors
Bias: Constrained Estimates and the Data Generating Parameters
Unbiased Estimation under a Constraint
A Plausible Constraint with Some Extra Empirical SupportGeometry of APC Models and Constrained Estimation
Introduction
General Geometric View of Rank Deficient by One Models
Generalization to Systems with More Dimensions
APC Model with Linearly Coded Variables
Equivalence of the Geometric and Algebraic Solutions
Geometry of the Multiple Classification Model
Distance from Origin and Distance along the Line of Solutions
Empirical Example: Frost’s Tuberculosis Data
Summarizing Some Important Features from the Geometry of APC Models
Problem with Mechanical ConstraintsEstimable Functions Approach
Introduction
Estimable Functions
l′sv Approach for Establishing Estimable Functions in APC Models
Some Examples of Estimable Functions Derived Using the l′sv Approach
Comments on the l′sv Approach
Estimable Functions with Empirical Data
More Substantive Examination of Differences of Male and Female Lung Cancer Mortality RatesPartitioning the Variance in APC Models
Introduction
Age?Period?Cohort Analysis of Variance (APC ANOVA) Approach to Attributing Variance
APC Mixed Model
Hierarchical APC Model
Empirical Example Using Homicide Offending DataFactor-Characteristic Approach
Introduction
Characteristics for One Factor
Characteristics for Two or More Factors
Variance Decomposition for Factors and for Factor Characteristics
Empirical Examples: Age?Period-Specific Suicide Rates and Frequencies
Age?Period?Cohort Characteristics (APCC) Analysis of Suicide Data with Two Cohort Characteristics
Age?Cohort?Period Characteristics (ACPC) Analysis of the Suicide Data with Two Period Characteristics
Age?Period?Characteristics?Cohort Characteristics Model
Approaches Based on Factor Characteristics and Mechanism
Additional Features and Analyses of Factor-Characteristic ModelsConclusions: An Empirical Example
Introduction
Empirical Example: Homicide OffendingIndex
Conclusions and References appear at the end of each chapter.
정보제공 :
