• 가정과삶의질연구
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• 제10권1호통권19호
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• pp.131-138
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• 1992

Regression analysis has become a standard statistical tool in the behavioral science. Because of its widespread popularity. regression has been often misused. Such is the case when the dependent variable is a qualitative measure rather than a continuous, interval measure. Regression estimates with a qualitative dependent variable does not meet the assumptions underlying regression. It can lead to serious errors in the standard statistical inference. Logit model is recommended as alternatives to the regression model for qualitative dependent variables. Researchers can employ this model to measure the relationship between independent variables and qualitative dependent variables without assuming that logit model was derived from probabilistic choice theory. Coefficients in logit model are typically estimated by the method of Maximum Likelihood Estimation in contrast to ordinary regression model which estimated by the method of Least Squares Estimation. Goodness of fit in logit model is based on the likelihood ratio statistics and the t-statistics is used for testing the null hypothesis.

• International Journal of Reliability and Applications
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• 제1권1호
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• pp.15-26
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• 2000

This paper considers an aperiodic preventive maintenance (PM) model for repairable systems, in which the time intervals between two consecutive preventive maintenances are unequal. To propose such an aperiodic PM model, we assume that each PM reduces the current hazard rate by a certain amount which depends on the number of PMs performed previously. If the system fails between PMs, the minimal repair is performed and the hazard rate remains unchanged after the repair. We give the exact expressions for the hazard rate function for the aperiodic PM model. Based on the proposed aperiodic PM model, we suggest the maximum likelihood method to estimate the parameters characterizing the model and apply the method to the case of Weibull distribution. Numerical examples for estimating the parameters are presented for the purpose of illustration.

• 대한전기학회논문지
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• 제38권12호
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• pp.971-982
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• 1989

An integrating position estimation algorithm has been developed for the navigation system of a free-ranging AGV system. The navigation system focused in this research work consists of redundant wheel encoders for the relative position measurement and a vision sensor for the absolute position measurement. A maximum likelihood method and an extended Kalman filter are implemented for enhancing the performance of the position estimator. The maximum likelihood estimator processes noisy, redundant wheel encoder measurements and yields efficient estimates for the AGV motion between each sampling interval. The extended Kalman filter fuses inharmonious positional data from the deadreckoner and the vision sensor and computes the optimal position estimate. The simulation results show that the proposed position estimator solves a generalized estimation problem for locating the vehicle accurately in space.

• Communications for Statistical Applications and Methods
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• 제24권1호
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• pp.29-41
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• 2017

The estimation problem of expected time to failure of units is studied in a discrete set up. A simple step-stress accelerated life testing is considered with a Type-I censored sample from geometric distribution that is a commonly used distribution to model the lifetime of a device in discrete case. Maximum likelihood estimators as well as the associated distributions are derived. Exact, approximate and bootstrap approaches construct confidence intervals that are compared via a simulation study. Optimal confidence intervals are suggested in view of the expected width and coverage probability criteria. An illustrative example is also presented to explain the results of the paper. Finally, some conclusions are stated.

• Communications for Statistical Applications and Methods
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• 제25권4호
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• pp.411-430
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• 2018

The Bayesian approach is a suitable alternative in constructing appropriate models for observed record values because the number of these values is small. This paper provides an objective Bayesian analysis method for upper record values arising from the Rayleigh distribution. For the objective Bayesian analysis, the Fisher information matrix for unknown parameters is derived in terms of the second derivative of the log-likelihood function by using Leibniz's rule; subsequently, objective priors are provided, resulting in proper posterior distributions. We examine if these priors are the PMPs. In a simulation study, inference results under the provided priors are compared through Monte Carlo simulations. Through real data analysis, we reveal a limitation of the appropriate confidence interval based on the maximum likelihood estimator for the scale parameter and evaluate the models under the provided priors.

• 응용통계연구
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• 제28권2호
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• pp.269-279
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• 2015

본 논문에서는 임의효과에 대한 추론 문제가 다루어졌으며 이 추론에서 신뢰분포를 사용하는 것이 제안되었다. 신뢰분포를 이용한 방법은 표본의 크기가 작아도 임의절편들이 있는 로지스틱 회귀분석에서 좋은 결과를 보여주었으며, 자료분석을 통해서도 각 개체가 가지는 임의효과들에 대한 세밀한 분석이 가능함을 확인하였다.

• 통합자연과학논문집
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• 제5권4호
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• pp.246-252
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• 2012

Multivariate cumulative sum (CUSUM) control charts for simultaneously monitoring both means and variances under multivariate normal process are investigated. Performances of multivariate CUSUM schemes are evaluated for matched fixed sampling interval (FSI) and variable sampling interval (VSI) features in terms of average time to signal (ATS), average number of samples to signal (ANSS). Multivariate Shewhart charts are also considered to compare the properties of multivariate CUSUM charts. Numerical results show that presented CUSUM charts are more efficient than the corresponding Shewhart chart for small or moderate shifts and VSI feature with two sampling intervals is more efficient than FSI feature. When small changes in the production process have occurred, CUSUM chart with small reference values will be recommended in terms of the time to signal.

• Journal of the Korean Statistical Society
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• 제36권2호
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• pp.321-333
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• 2007

In the proportional hazard model with the beta process prior, the posterior computation with the discrete approximation is considered. The time period of interest is partitioned by small intervals. On each partitioning interval, the likelihood is approximated by that of a binomial experiment and the beta process prior is by a beta distribution. Consequently, the posterior is approximated by that of many independent binomial model with beta priors. The analysis of the leukemia remission data is given as an example. It is illustrated that the length of the partitioning interval affects the posterior and one needs to be careful in choosing it.

• Journal of the Korean Data and Information Science Society
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• 제7권1호
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• pp.21-35
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• 1996

In this paper, estimation and prediction procedures are discussed for grneral situation in which the failure time follows the independent density $f_{i}({\varepsilon}_{i})$ for the accelerated life testing under Type II censoring. In the context of accelerated life test experiment, procedures are given for estimating the parameters in the Eyring model, and for estimating mean life at a given future stress level. The procedures given are conditional confidence interval procedures, obtained by conditioning on ancillary statistics. A comparison is made of these procedures and procedures based on asymptotic properties of the maximum, likelihood estimates.

• 응용통계연구
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• 제25권3호
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• pp.403-413
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• 2012

The modifications suggested in Uhm et al. (2011) are studied using a partly parametric version of Aalen's additive risk model. A follow-up time period is partitioned into intervals, and hazard functions are estimated as a piecewise constant in each interval. A maximum likelihood estimator by iteratively reweighted least squares and variance estimates are suggested based on the model as well as evaluated by simulations using mean square error and a coverage probability, respectively. In conclusion the modifications are needed when there are a small number of uncensored deaths in an interval to estimate the piecewise constant hazard function.