latent structure analysis;
grade of membership;
population heterogeneity;
mortality;
NLTCS data;
disability;
simulation studies;
MEMBERSHIP;
HEALTH;
GRADE;
MORTALITY;
D O I:
10.1080/17486700802259798
中图分类号:
Q [生物科学];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
Linear latent structure analysis is a new approach for investigation of population heterogeneity using high-dimensional categorical data. In this approach, the population is represented by a distribution of latent vectors, which play the role of heterogeneity variables, and individual characteristics are represented by the expectation of this vector conditional on individual response patterns. Results of the computer experiments demonstrating a good quality of reconstruction of model parameters are described. The heterogeneity distribution estimated from 1999 National Long Term Care Survey (NLTCS) is discussed. A predictive power of the heterogeneity scores on mortality is analysed using vital statistics data linked to NLTCS.
机构:
Georgia State Univ, Dept Risk Management & Insurance, Atlanta, GA 30303 USAGeorgia State Univ, Dept Risk Management & Insurance, Atlanta, GA 30303 USA
Peng, Liang
Xiao, Hongmin
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机构:
Northwest Normal Univ, Coll Math & Stat, Lanzhou 730000, Gansu, Peoples R ChinaGeorgia State Univ, Dept Risk Management & Insurance, Atlanta, GA 30303 USA
机构:
Univ Stellenbosch, Dept Stat & Actuarial Sci, ZA-7600 Stellenbosch, South AfricaUniv Stellenbosch, Dept Stat & Actuarial Sci, ZA-7600 Stellenbosch, South Africa
Le Roux, Niel J.
Gardner-Lubbe, Sugnet
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机构:
Univ Cape Town, Dept Stat Sci, ZA-7925 Cape Town, South AfricaUniv Stellenbosch, Dept Stat & Actuarial Sci, ZA-7600 Stellenbosch, South Africa
Gardner-Lubbe, Sugnet
Gower, John C.
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机构:
Open Univ, Dept Math & Stat, Milton Keynes MK7 6AA, Bucks, EnglandUniv Stellenbosch, Dept Stat & Actuarial Sci, ZA-7600 Stellenbosch, South Africa