Search Results - "Kozumi, Hideo"

Bayesian analysis of quantile regression for censored dynamic panel data by Kobayashi, Genya, Kozumi, Hideo

“…This paper develops a Bayesian approach to analyzing quantile regression models for censored dynamic panel data. We employ a likelihood-based approach using…”
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Generalized multiple-point Metropolis algorithms for approximate Bayesian computation by Kobayashi, Genya, Kozumi, Hideo

“…It is well known that the approximate Bayesian computation algorithm based on Markov chain Monte Carlo methods suffers from the sensitivity to the choice of…”
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Does yardstick regulation really work? Empirical evidence from Japan's rail industry by Mizutani, Fumitoshi, Kozumi, Hideo, Matsushima, Noriaki

“…This paper investigates the effectiveness of yardstick regulation by using a data set of rail companies in Japan and estimating the variable frontier cost…”
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Gibbs sampling methods for Bayesian quantile regression by Kozumi, Hideo, Kobayashi, Genya

“…This paper considers quantile regression models using an asymmetric Laplace distribution from a Bayesian point of view. We develop a simple and efficient Gibbs…”
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AN INTEGRATED PURCHASE MODEL USING GAUSSIAN COPULA by Kobayashi, Genya, Kakamu, Kazuhiko, Sato, Eisaku, Kozumi, Hideo

“…This paper proposes an integrated purchase model of household category purchase incidence, brand choice, and purchase quantity choice using the Gaussian…”
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Estimation of Lorenz curves: a Bayesian nonparametric approach by Hasegawa, Hikaru, Kozumi, Hideo

“…In this article, we estimate Lorenz curves using the recent development of the Bayesian nonparametric method with Dirichlet process prior. We also consider…”
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The exact general formulae for the moments and the MSE dominance of the Stein-rule and positive-part Stein-rule estimators by Ohtani, Kazuhiro, Kozumi, Hideo

“…In this paper, we derive the general formulae for the moments of a linear functional of the Stein-rule and positive-part Stein-rule estimators (i.e., h′ b SR…”
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Analysts' Perceptions of Intellectual Capital Information by Sakakibara, Shigeki, Hansson, Bo, Yosano, Tadanori, Kozumi, Hideo

“…This study is based on a survey of 324 financial analysts in Japan. The survey concerns analysts’ perceptions of intellectual capital (IC) information and its…”
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Posterior analysis of latent competing risk models by parallel tempering by Kozumi, Hideo

“…Latent competing risk models are examined from a Bayesian point of view. The parallel tempering algorithm is applied for posterior inference and compared with…”
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Bayesian analysis on Engel curves estimation with measurement errors and an instrument variable by Hasegawa, Hikaru, Kozumi, Hideo

“…This paper considers the Bayesian estimation of Engel curves specified as the Working-Leser form with the measurement errors on both the left and the right…”
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A Bayesian Analysis of Structural Changes with an Application to the Displacement Effect by Kozumi, Hideo, Hasegawa, Hikaru

“…In this paper we propose a new approach to the problem of structural change from a Bayesian point of view. Our approach is based on a hierarchical model with a…”
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Bayesian and non-bayesian analysis of gamma stochastic frontier models by Markov Chain Monte Carlo methods by Kozumi, Hideo, Zhang, Xingyuan

“…This paper considers simulation-based approaches for the gamma stochastic frontier model. Efficient Markov chain Monte Carlo methods are proposed for sampling…”
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Bayesian Analysis of Discrete Survival Data with a Hidden Markov Chain by Kozumi, Hideo

“…This paper considers the discrete survival data from a Bayesian point of view. A sequence of the baseline hazard functions, which plays an important role in…”
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Bayesian Analysis on Engel Curves Estimation With Measurement Errors and an Instrumental Variable by Hasegawa, Hikaru, Kozumi, Hideo

“…In this article, we consider the Bayesian estimation of Engel curves specified as the Working-Leser form with the measurement errors on both the left and the…”
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Testing for negativity in a demand system: A Bayesian approach by Hasegawa, Hikaru, Kozumi, Hideo, Hashimoto, Noriko

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