Seong W. Kim

Bayesian single change point detection in a sequence of multivariate normal observations

A Bayesian method is used to see whether there are changes of mean, covariance, or both at an unknown time point in a sequence of independent multivariate normal observations. Noninformative priors are used for all competing models: no-change model, mean change model, covariance change model, and mean and covariance change model. We use several versions of the intrinsic Bayes factor of Berger and Pericchi (Berger, J.O. and Pericchi, L.R., 1996, The intrinsic Bayes factor for model selection and prediction. Journal of the American Statistical Association,\/ 91, 109–122; Berger, J.O. and Pericchi, L.R., 1998, Accurate and stable Bayesian model selection: the median intrinsic Bayes factor. Sankhya Series B, 60, 1–18.) to detect a change point. We demonstrate our results with some simulated datasets and a real dataset.

Estimation of the optimal number of cluster-heads in sensor network

A sensor network system consisting of a large number of small sensors with low-power can be an effective tool for collection and integration of data by each sensor in a variety of environments. The collected data by each sensor node is communicated through the network to a single base station that uses all collected data to determine properties of the data. Clustering sensors into groups, yields that sensors communicate information only to cluster heads and then the cluster-heads communicate the aggregated information to the base station. We estimate the optimal number of cluster-heads among randomized sensors in a bounded region. We derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the wireless sensor network when all sensors communicate data from the cluster-heads to the base station. Computer simulation shows that the energy consumption reduce as the optimal number of cluster-heads for the proposed method.

Fisher information in progressive hybrid censoring schemes

The hybrid censoring scheme, which is a mixture of Type-I and Type-II censoring schemes, has been extended to the case of progressive censoring schemes by Kundu and Joarder [Analysis of Type-II progressively hybrid censored data, Comput. Stat. Data Anal. 50 (2006), pp. 2509–2528] and Childs et al. [Exact likelihood inference for an exponential parameter under progressive hybrid censoring schemes, in Statistical Models and Methods for Biomedical and Technical Systems, F. Vonta, M. Nikulin, N. Limnios, and C. Huber-Carol, eds., Birkhäuser, Boston, MA, 2007, pp. 323–334]. In this paper, we derive a simple expression for the Fisher information contained in Type-I and Type-II progressively hybrid censored data. An illustrative example is provided applicable to a scaled-exponential distribution to demonstrate our methodologies.

Bayesian approach with the power prior for road safety analysis

Drawing inference from current data could be more reliable if similar data based on previous studies are used. We propose a full Bayesian approach with the power prior to utilize these data. The power prior is constructed by raising the likelihood function of the historical data to the power where . The power prior is a useful informative prior in Bayesian inference. We use the power prior to estimate regression coefficients and to calculate the accident reduction factors of some covariates including median strips and guardrails. We also compare our method with the empirical Bayes method. We demonstrate our results with several sets of real data. The data were collected for two rural national roads of Korea in the year 2002. The computations are executed with the Metropolis–Hastings algorithm which is a popular technique in the Markov chain and Monte Carlo methods.

Estimation of Parameters in a Bivariate Generalized Exponential Distribution Based on Type-II Censored Samples

In this article, we discuss the maximum likelihood estimation and Bayesian estimation procedures for estimating the parameters in an absolute continuous bivariate generalized exponential distribution based on Type-II censored samples. A Markov chain Monte Carlo method is applied to compute the Bayes estimates. We also propose a method to obtain the initial estimates of the parameters for the required iterative algorithm. A simulation study is used to evaluate the performance of the proposed estimation procedures. Two real data examples are utilized to illustrate the methodology developed in this manuscript.

Possible line edge roughness reduction by anisotropic molecular resist

Extreme ultra-violet (EUV) lithography technology is being developed for the patterning of sub-22nm node. Line edge roughness (LER) is the one of the important issues together with the resist performance like resolution and sensitivity. There are some novel resists for EUV lithography that can be used for obtaining the target resolution and sensitivity, while the line edge roughness do not reached the target values in most resist yet. In order to reduce the LER, the molecular resist has been widely studied due to their small size compared to the conventional polymer resist. There is another approach to reduce the LER by reducing the acid diffusion length, but it is not easy to reduce down the acid diffusion length. We tried a new approach to reduce down the LER by changing the shape or structure of the molecular resist. A new molecular resist shape that shows the anisotropic structure is tried to see the LER and whether this anisotropic resist can be used for LER reduction. It turns out that the LER is minimum when the molecular chain alignment is along the depth, while LER is maximum when the molecular chain is randomly distributed.