• Title/Summary/Keyword: Multiprocess Dynamic Model

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BAYESIAN ESTIMATION PROCEDURES IN MULTIPROCESS DISCOUNT NORMAL MODEL

  • Sohn, Joong-Kweon;Kang, Sang-Gil;Kim, Heon-Joo
    • Journal of the Korean Data and Information Science Society
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    • v.6 no.2
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    • pp.29-39
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    • 1995
  • A model used in the past may be altered at will in modeling for the future. For this situation, the multiprocess dynamic model provides a general framework. In this paper we consider the multiprocess discount normal model with parameters having a time dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.

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Finite Population Prediction under Multiprocess Dynamic Generalized Linear Models

  • Kim, Dal-Ho;Cha, Young-Joon;Lee, Jae-Man
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.2
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    • pp.329-340
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    • 1999
  • We consider a Bayesian forcasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under multiprocess dynamic generalized linear models. The multiprocess dynamic model offers a powerful framework for the modelling and analysis of time series which are subject to a abrupt changes in pattern. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

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Multiprocess Dynamic Poisson Mode1s: The Covariates Case

  • Shim, Joo-Yong;Sohn, Joong-Kweon
    • Journal of the Korean Statistical Society
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    • v.27 no.3
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    • pp.279-288
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    • 1998
  • We propose a multiprocess dynamic Poisson model for the analysis of Poisson process with the covariates. The algorithm for the recursive estimation of the parameter vector modeling time-varying effects of covariates is suggested. Also the algorithm for forecasting of numbers of events at the next time point based on the information gathered until the current time is suggested.

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Bayesian Estimation Procedure in Multiprocess Discount Generalized Model

  • Joong Kweon Sohn;Sang Gil Kang;Joo Yong Shim
    • Communications for Statistical Applications and Methods
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    • v.4 no.1
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    • pp.193-205
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    • 1997
  • The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we consider the multiprocess discount generalized model with parameters having a dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt change of pattern in parameters.

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Bayesian Estimation Procedure in Multiprocess Non-Linear Dynamic Normal Model

  • Sohn, Joong-Kweon;Kang, Sang-Gil
    • Communications for Statistical Applications and Methods
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    • v.3 no.1
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    • pp.155-168
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    • 1996
  • In this paper we consider the multiprocess dynamic normal model with parameters having a time dependent non-linear structure. We develop and study the recursive estimation procedure for the proposed model with normality assumption. It turns out thst the proposed model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.

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Multiprocess Dynamic Survival Models with Numbers of Deaths

  • Joo Yong Shim;Joong Kweon Sohn;Sang Gil Kang
    • Journal of the Korean Statistical Society
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    • v.25 no.4
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    • pp.567-576
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    • 1996
  • The multiprocess dynamic survival model is proposed for the application of the regression model on the analysis of survival data with time-varying effects of covariates : where the survival data consists of numbers of deaths at certain time-points. The algorithm for the recursive estimation of a time-varying parameter vector is suggested. Also the algorithm of forecasting of numbers of deaths of each group in the next time interval based on the information gathered until the end of current time interval is suggested.

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Multiprocess Discount Survival Models With Survival Times

  • Shim, Joo-Yong
    • Journal of the Korean Statistical Society
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    • v.26 no.2
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    • pp.277-288
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    • 1997
  • For the analysis of survival data including covariates whose effects vary in time, the multiprocess discount survival model is proposed. The parameter vector modeling the time-varying effects of covariates is to vary between time intervals and its evolution between time intervals depends on the perturbation of the next time interval. The recursive estimation of the parameter vector can be obtained at the end of each time interval. The retrospective estimation of the survival function and the forecasting of the survival function of individuals of the specific covariates also can be obtained based on the information gathered until the end of the time interval.

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