• Title/Summary/Keyword: Hierarchical Function

Search Result 397, Processing Time 0.036 seconds

A Bayesian Method to Semiparametric Hierarchical Selection Models (준모수적 계층적 선택모형에 대한 베이지안 방법)

  • 정윤식;장정훈
    • The Korean Journal of Applied Statistics
    • /
    • v.14 no.1
    • /
    • pp.161-175
    • /
    • 2001
  • Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. Hierarchical models including selection models are introduced and shown to be useful in such Bayesian meta-analysis. Semiparametric hierarchical models are proposed using the Dirichlet process prior. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierachical selection model with including unknown weight function and use Markov chain Monte Carlo methods to develop inference for the parameters of interest. Using Bayesian method, this model is used on a meta-analysis of twelve studies comparing the effectiveness of two different types of flouride, in preventing cavities. Clinical informative prior is assumed. Summaries and plots of model parameters are analyzed to address questions of interest.

  • PDF

A Production Function for the Organization with Hierarchical Network Queue Structure (계층적(階層的) 네트웍 대기구조(待機構造)를 갖는 조직(組織)의 생산함수(生産函數)에 대한 연구(硏究))

  • Gang, Seok-Hyeon;Kim, Seong-In
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.12 no.1
    • /
    • pp.63-71
    • /
    • 1986
  • In the organization with a hierarchical network queue structure a production function is derived whose input factors are the numbers of servers at nodes and output is the number of served customers. Its useful properties are investigated. Using this production function, the contributions of servers to the number of served customers are studied. Also given an expected waiting time in the system for each customer, the optimal numbers of servers at nodes are obtained minimizing a cost function.

  • PDF

Sampling Based Approach to Hierarchical Bayesian Estimation of Reliability Function

  • Younshik Chung
    • Communications for Statistical Applications and Methods
    • /
    • v.2 no.2
    • /
    • pp.43-51
    • /
    • 1995
  • For the stress-strengh function, hierarchical Bayes estimations considered under squared error loss and entropy loss. In particular, the desired marginal postrior densities ate obtained via Gibbs sampler, an iterative Monte Carlo method, and Normal approximation (by Delta method). A simulation is presented.

  • PDF

An Investigation of the Use of Hierarchical Elements for Incompressible Flow Computations (비압축성 유동계산을 위한 계층 요소 사용의 검토)

  • Kim, Jin-Hwan;Jeong, Chang-Ryul
    • Transactions of the Korean Society of Mechanical Engineers B
    • /
    • v.26 no.9
    • /
    • pp.1209-1217
    • /
    • 2002
  • The use of a two dimensional hierarchical elements are investigated for the incompressible flow computation. The construction of hierarchical elements are explained by both a geometric configuration and a determination of degrees of freedom. Also a systematic treatment of essential boundary values has been developed for the degrees of freedom corresponding to higher order terms. The numerical study for the poisson problem shows that the computation with hierarchical higher order elements can increase the convergence rate and accuracy of finite element solutions in more efficient manner than the use of standard first order element. for Stokes and Cavity flow cases, a mixed version of penalty function approach has been introduced in connection with the hierarchical elements. Solutions from hierarchical elements showed better resolutions with consistent trends in both mesh shapes and the order of elements.

Empirical Bayes Estimate for Mixed Model with Time Effect

  • Kim, Yong-Chul
    • Communications for Statistical Applications and Methods
    • /
    • v.9 no.2
    • /
    • pp.515-520
    • /
    • 2002
  • In general, we use the hierarchical Poisson-gamma model for the Poisson data in generalized linear model. Time effect will be emphasized for the analysis of the observed data to be collected annually for the time period. An extended model with time effect for estimating the effect is proposed. In particularly, we discuss the Quasi likelihood function which is used to numerical approximation for the likelihood function of the parameter.

Bayesian Hierarchical Model with Skewed Elliptical Distribution

  • Chung Younshik
    • Proceedings of the Korean Statistical Society Conference
    • /
    • 2000.11a
    • /
    • pp.5-12
    • /
    • 2000
  • Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution and it is shown to be useful in such Bayesian meta-analysis. A general class of skewed elliptical distribution is reviewed and developed. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierarchical selection model and use Markov chain Monte Carlo methods to develop inference for the parameters of interest.

  • PDF

A hierarchical approach to state estimation of time-varying linear systems via block pulse function (블럭펄스함수를 이용한 시스템 상태추정의 계층별접근에 관한 연구)

  • 안두수;안비오;임윤식;이재춘
    • The Transactions of the Korean Institute of Electrical Engineers
    • /
    • v.45 no.3
    • /
    • pp.399-406
    • /
    • 1996
  • This paper presents a method of hierarchical state estimation of the time-varying linear systems via Block-pulse function(BPF). When we estimate the state of the systems where noise is considered, it is very difficult to obtain the solutions because minimum error variance matrix having a form of matrix nonlinear differential equations is included in the filter gain calculation. Therefore, hierarchical approach is adapted to transpose matrix nonlinear differential equations to a sum of low order state space equation from and Block-pulse functions are used for solving each low order state space equation in the form of simple and recursive algebraic equation. We believe that presented methods are very attractive nd proper for state estimation of time-varying linear systems on account of its simplicity and computational convenience. (author). 13 refs., 10 figs.

  • PDF

One-Pot Electrochemical Synthesis of Hierarchical Porous Niobium

  • Joe, Gihwan;Shin, Heon-Cheol
    • Journal of Electrochemical Science and Technology
    • /
    • v.12 no.2
    • /
    • pp.257-265
    • /
    • 2021
  • In this study, we report niobium (Nb) with hierarchical porous structure produced by a one-pot, HF-free electrochemical etching process. It is proved experimentally that a well-defined hierarchical porous structure is produced from the combination of a limited repetition of pulse etching and high concentration of aggressive anion (i.e., SO42-), which results in hierarchical pores with high order over 3. A formula is derived for the surface area of porous Nb as a function of the hierarchical order of pores while the experimental surface area is estimated on the basis of the electrochemical gas evolution rate on porous Nb. From the comparison of the theoretical and experimental surface areas, an in-depth understanding was gained about porous structure produced in this work in terms of the actual pore shape and hierarchical pore order.

Semiparametric Bayesian Estimation under Structural Measurement Error Model

  • Hwang, Jin-Seub;Kim, Dal-Ho
    • Communications for Statistical Applications and Methods
    • /
    • v.17 no.4
    • /
    • pp.551-560
    • /
    • 2010
  • This paper considers a Bayesian approach to modeling a flexible regression function under structural measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under structural measurement error model without a semiparametric component.

Semiparametric Bayesian estimation under functional measurement error model

  • Hwang, Jin-Seub;Kim, Dal-Ho
    • Journal of the Korean Data and Information Science Society
    • /
    • v.21 no.2
    • /
    • pp.379-385
    • /
    • 2010
  • This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.