• Journal of Korean Society for Quality Management
• /
• v.37 no.4
• /
• pp.23-30
• /
• 2009

When the measurement method for a performance variable is destructive or expensive, it is profitable to replace the performance variable with a highly correlated surrogate variable. In this paper we propose screening procedures using a surrogate variable with specified producer's and consumer's risks. Blending the basic concepts of acceptance sampling plans and screening procedures, the proposed model can be used effectively by quality professionals. Two models are considered: the normal model with dichotomous performance and continuous surrogate variables, and the bivariate normal model with continuous performance and surrogate variables. It is assumed the surrogate variable given the performance variable is normally distributed in the normal model, and performance and surrogate variables are jointly normally distributed in the bivariate normal model. For the two models, producer's and consumer's risks are derived, and methods of finding the optimal screening procedures are presented. Numerical examples are also given.

• Communications for Statistical Applications and Methods
• /
• v.29 no.3
• /
• pp.373-391
• /
• 2022

The distribution of observations in most econometric studies with spatial heterogeneity is skewed. Usually, a single transformation of the data is used to approximate normality and to model the transformed data with a normal assumption. This assumption is however not always appropriate due to the fact that panel data often exhibit non-normal characteristics. In this work, the normality assumption is relaxed in spatial mixed models, allowing for spatial heterogeneity. An inference procedure based on Bayesian mixed modeling is carried out with a multivariate skew-elliptical distribution, which includes the skew-t, skew-normal, student-t, and normal distributions as special cases. The methodology is illustrated through a simulation study and according to the empirical literature, we fit our models to non-life insurance consumption observed between 1998 and 2002 across a spatial panel of 103 Italian provinces in order to determine its determinants. Analyzing the posterior distribution of some parameters and comparing various model comparison criteria indicate the proposed model to be superior to conventional ones.

• Communications for Statistical Applications and Methods
• /
• v.15 no.2
• /
• pp.161-171
• /
• 2008

This study discusses Johnson's $S_U$-normal distribution capturing a wide range of non-normality in various regression models. We provide the likelihood inference using Johnson's $S_U$-normal distribution, and propose a likelihood ratio (LR) test for normality. We also apply the $S_U$-normal distribution to the binary and censored regression models. Monte Carlo simulations are used to show that the LR test using the $S_U$-normal distribution can be served as a model specification test for normal error distribution, and that the $S_U$-normal maximum likelihood (ML) estimators tend to yield more reliable marginal effect estimates in the binary and censored model when the error distributions are non-normal.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.23 no.4
• /
• pp.1113-1120
• /
• 1998

This paper introduces a channel model that is a combination of Rice and log-normal statistics, with independent shadowing affectingeach direct and diffuse component, repectively. This model extends the channel model of a combined Rice and Log-normal, proposed by Corazza, to include the independent shadowing. The validity of model is confirmed by comparisons with the data collectedin the literature, the analytical model, and the computer model in terms of probability distribution of the evvelope of each model. The model turns out to be one of many well-known narrowband models in limiting cases, e.g. Rayleigh, Rice, log-normal, Suzuki, Loo, and Corazza. Finally, the examples of bit error probability evaluations for several values of the elevation angle in the channel are provided.

• Journal of Power Electronics
• /
• v.19 no.4
• /
• pp.989-999
• /
• 2019

This paper proposes a fault-tolerant control strategy with finite control set model predictive control (FCS-MPC) based on hierarchical optimization for five-level H-bridge neutral-point-clamped (5L-HNPC) inverter-fed induction motor drives. Fault-tolerant operation is analyzed, and the fault-tolerant control algorithm is improved. Adopting FCS-MPC based on hierarchical optimization, where the voltage is used as the controlled objective, called model predictive voltage control (MPVC), the postfault controller is simplified as a two layer control. The first layer is the voltage jump limit, and the second layer is the voltage following control, which adopts the optimal control strategy to ensure the current following performance and uniqueness of the optimal solution. Finally, simulation and experimental results verify that 5L-HNPC inverter-fed induction motor drives have strong fault tolerant capability and that the FCS-MPVC based on hierarchical optimization is feasible.

• Journal of the Korean Data and Information Science Society
• /
• v.9 no.2
• /
• pp.165-172
• /
• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.

• Communications for Statistical Applications and Methods
• /
• v.14 no.3
• /
• pp.687-698
• /
• 2007

A normal mixture model with which dependence between classes is incorporated is proposed in order to detect differentially expressed genes. Gene clustering approaches suffer from the high dimensional column of microarray expression data matrix which leads to the over-fit problem. Various methods are proposed to solve the problem. In this paper, use of simple averaging data within each class is proposed to overcome the various problems due to high dimensionality when the normal mixture model is fitted. Some experiments through simulated data set and real data set show its availability in actuality.

• Journal of Technologic Dentistry
• /
• v.41 no.3
• /
• pp.211-217
• /
• 2019

Purpose: The purpose of this study is to evaluate the repeated measurement stability of scans related to dentition type. Methods: A normal model and the crowding and diastema models are also duplicated using duplicating silicon. After that, a plaster model is made using a plaster-type plaster on the duplicate mold, and each model is scanned 5 times by using an extraoral scanner. The gingival part and molar part were deleted from the 3D STL file data obtained through scanning. Using the 3D stl file obtained in this way, data is nested between model groups. Thereafter, RMS values obtained were compared and evaluated. The normality test of the data was performed for the statistical application of repeated measurements with dentition type, and the normality was satisfied. Therefore, the one-way ANOVA test, which is a parametric statistical method, was applied, and post-tests were processed by the Scheffe method. Results: The average size of each RMS in the Normal, Diastema, and Crowding groups was Normal> Crowding> Diastema. However, the standard deviation was in the order of Crowding> Normal> Diastema. The average value of each data is as follows. Diastema model was the smallest ($5.51{\pm}0.55{\mu}m$), followed by the crowding model ($12.30{\pm}2.50{\mu}m$). The normal model showed the maximum error ($13.23{\pm}1.06{\mu}m$). Conclusion: There was a statistically significant difference in the repeatability of the scanning measurements according to the dentition type. Therefore, you should be more careful when scanning the normal intense or crowded dentition than scanning the interdental lining. However, this error value was within the range of applicable errors for all clinical cases.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.2
• /
• pp.112-119
• /
• 2003

This paper proposes a model of SP(silent period) generation in masseter muscle by means of computer simulation. The model is based on the anatomical and physiological properties of trigeminal nervous system. In determining the SP generation pathway, evoked SPs of masseter muscle after mechanical stimulation to the chin are divided into normal and abnormal group. Normal SP is produced by the activation of mechanoreceptors in periodontal ligament. The activation of nociceptors contributes to the latter part of normal SP, abnormal extended SP is produced. As a result, the EMG signal generated by a proposed SP generation model is similar to both real EMG signal including normal SP and abnormal extended SP with TMJ patients. The result of this study have shown differences of SP generation mechanism between subjects both with and without TMJ dysfunction.

• The Korean Journal of Applied Statistics
• /
• v.24 no.2
• /
• pp.425-433
• /
• 2011

Reject inference in credit scoring is a statistical approach to adjust for nonrandom sample bias due to rejected applicants. Function estimation approaches are based on the assumption that rejected applicants are not necessary to be included in the estimation, when the missing data mechanism is missing at random. On the other hand, the density estimation approach by using mixture models indicates that reject inference should include rejected applicants in the model. When mixture models are chosen for reject inference, it is often assumed that data follow a normal distribution. If data include missing values, an application of the normal mixture model to fully observed cases may cause another sample bias due to missing values. We extend reject inference by a multivariate normal mixture model to handle incomplete characteristic variables. A simulation study shows that inclusion of incomplete characteristic variables outperforms the function estimation approaches.