• Journal of applied mathematics & informatics
• /
• v.3 no.1
• /
• pp.55-64
• /
• 1996

Let $X_1, ....$X_P be p($\geq$2) independent random variables, where each X1 has a gamma distribution with $k_i and ${\heta}_i$. The problem is to simultaneously estimate p gammar parameters ${\heta}_i$ under entropy loss where the parameters are believed priori. Hierarchical bayes(HB) and empirical bayes(EB) estimators are investigated. Next computer simulation is studied to compute the risk percentage improvement of the HB, EB and the estimator of Dey et al.(1987) compared to MVUE of ${\heta}$.

• Communications for Statistical Applications and Methods
• /
• v.8 no.1
• /
• pp.109-115
• /
• 2001

Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.

• Asia Marketing Journal
• /
• v.8 no.1
• /
• pp.1-17
• /
• 2006

A couple of previous studies have warned against the use of homogeneous choice models in assessing the effect of reference price since unaccounted for response heterogeneity may result in spurious reference price effects(Chang, Siddarth and Weinberg 1999; Bell and Lattin 2000). According to Meyer and Kahn(1991), not accounting for consideration set heterogeneity may also bias the effect parameters in the choice model. Therefore, failure to account for these two sources of bias, in fact, have cast doubt on the empirical support for reference price effects in general. In view of aforementioned potential sources of bias, the author investigates the robustness of loss aversion effect in the reference-dependent model after accounting for heterogeneity in response as well as consideration set. The proposed model defines individual household's consideration set based on the posterior distribution of preference obtained from the Empirical Bayes approach. In addition, the same posterior distribution is used to form household-specific reference prices. Response heterogeneity correction is carried out via the Latent Class approach. The proposed model outperforms the Reference-Dependent model that includes the reference price measure most often employed in the previous studies. This implies that as a way of simplifying decision task, consumers restrict their consideration set to a subset of available brands not only in making a brand choice but also in forming reference prices.

• The Korean Journal of Applied Statistics
• /
• v.7 no.1
• /
• pp.159-172
• /
• 1994

An empirical Bayes estimator of the probability of discovering a new species is proposed when some prior information is available on the number f species. The new estimator is shown via simulations to have only a moderate bias and a smaller RMSE than Good's estimator when the species population follows a truncated geometric distribution.

• Journal of the Korean Statistical Society
• /
• v.25 no.4
• /
• pp.529-544
• /
• 1996

This paper deals with the problem of estimating the autoregressive random coefficient of a first-order random coefficient autoregressive time series model applied to panel data of time series. The autoregressive random coefficients across individual units are assumed to be a random sample from a truncated normal distribution with the space (-1, 1) for stationarity. The estimates of random coefficients are obtained by an empirical Bayes procedure using the estimates of model parameters. Also, a Monte Carlo study is conducted to support the estimation procedure proposed in this paper. Finally, we apply our results to the economic panel data in Liu and Tiao(1980).

• Journal of the Korean Statistical Society
• /
• v.32 no.2
• /
• pp.121-150
• /
• 2003

The National Health Interview Survey (NHIS) is one of the surveys used to assess the health status of the US population. One indicator of the nation's health is the total number of doctor visits made by the household members in the past year, There is a substantial nonresponse among the sampled households, and the main issue we address here is that the nonrespones mechanism should not be ignored because respondents and nonrespondents differ. It is standard practice to summarize the number of doctor visits by the binary variable of no doctor visit versus at least one doctor visit by a household for each of the fifty states and the District of Columbia. We consider a nonignorable nonresponse model that expresses uncertainty about ignorability through the ratio of odds of a household doctor visit among respondents to the odds of doctor visit among all households. This is a hierarchical model in which a nonignorable nonresponse model is centered on an ignorable nonresponse model. Another feature of this model is that it permits us to "borrow strength" across states as in small area estimation; this helps because some of the parameters are weakly identified. However, for simplicity we assume that the hyperparameters are fixed but unknown, and these hyperparameters are estimated by the EM algorithm; thereby making our method Bayes empirical Bayes. Our main result is that for some of the states the nonresponse mechanism can be considered non-ignorable, and that 95% credible intervals of the probability of a household doctor visit and the probability that a household responds shed important light on the NHIS.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.9 no.2
• /
• pp.23-32
• /
• 2010

Because ROTA(road traffic authority) analyzes the effects of accident reduction based on the data of 1-year after installation of RLC(red light camera), study of accident reduction effects over year after the installation of RLC is very short. This study deals with the traffic accident reduction during 3 years after the installation of RLC. The objective is to analyze the effects of accident reduction by year using EB method. In pursuing the above, the study uses the 951 accident data occurred at the 20 intersections which RLC are installed. The main results analyzed are as follows. First, the safety performance function (SPF) has been developed by the Poisson regression models which are statistically significant. Second, the results of an Empirical Bayes(EB) analyses showed that the accidents were reduced by the range from 2.73 to 38.75% after 1 year, from 6.85 to 47.36% after 2 year, and from 6.04 to 39.31% after 3 year from the installation of RLC.

• The Korean Journal of Applied Statistics
• /
• v.22 no.3
• /
• pp.443-461
• /
• 2009

In order to overcome the lack of Korean credit rating migration data, we consider an empirical Bayes procedure to estimate credit rating migration matrices. We derive the posterior probabilities of Korean credit rating transitions by utilizing the Moody's rating migration data and the credit rating assignments from Korean rating agency as prior information and likelihood, respectively. Metrics based upon the average transition probability are developed to characterize the migration matrices and compare our Bayesian migration matrices with some given matrices. Time series data for the metrics show that our Bayesian matrices are stable, while the matrices based on Korean data have large variation in time. The bootstrap tests demonstrate that the results from the three estimation methods are significantly different and the Bayesian matrices are more affected by Korean data than the Moody's data. Finally, Monte Carlo simulations for computing the values of a portfolio and its credit VaRs are performed to compare these migration matrices.

• Communications for Statistical Applications and Methods
• /
• v.13 no.1
• /
• pp.141-150
• /
• 2006

Penalized likelihood regression for exponential families have been considered by Kim (2005) through smoothing parameter selection and asymptotically efficient low dimensional approximations. We derive approximate Bayesian confidence intervals based on Bayes model associated with lower dimensional approximations to provide interval estimates in penalized likelihood regression and conduct empirical studies to access their properties.

• International Journal of Highway Engineering
• /
• v.15 no.4
• /
• pp.135-146
• /
• 2013

PURPOSES : This study aimed to analyze the impact the operation of pre-signals at 4-leg signalized intersections and present primary environmental factors of roads that need to be considered in the installation of pre-signals. METHODS : Shift of proportions safety effectiveness evaluation method which assesses shifts in proportions of target collision types to determine safety effectiveness was applied to analyze traffic crash by types. Also, Empirical Bayes before/after safety effectiveness evaluation method was adapted to analyze the impact pre-signal installation. Negative binomial regression was conducted to determine SPF(safety performance function). RESULTS : Pre-signals are effective in reducing the number of head on, right angle and sideswipe collisions and both the total number of personal injury crashes and severe crashes. Also, it is deemed that each factor used as an independent variable for the SPF model has strong correlation with the total number of personal injury crashes and severe crashes, and impacts general traffic crashes as a whole. CONCLUSIONS: This study suggests the following should be considered in pre-signal installation on intersections. 1) U-turns allowed in the front and rear 2) A high number of roads that connect to the intersection 3) Many right-turn traffic flows 4) Crosswalks installed in the front and rear 5) Insufficient left-turn lanes compared to left-turn traffic flows or no left-turn-only lane.