• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.633-641
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• 2001

The least squares method is usually applied when estimating the parameters in the regression models. However the least square estimator is not very efficient when the distribution of the error is skewed. In this paper, we propose the asymmetric least square estimator for a particular nonlinear time series regression model, and give the simple and practical sufficient conditions for the strong consistency of the estimators.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.219-227
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• 2001

Another approach (multi-parameter measurement method) of interlaboratory studies of test methods is presented. When the unrestricted normal likelihood for the fixed latent variable model is unbounded, we propose a me쇙 of restricting the parameter space by formulating realistic alternative hypothesis under which the likelihood is bounded. A simulation study verified the claim of conservatism of level of significance based on assumptions about central chi-square distributed test statistics and on Bonferroni approximations. We showed a randomization approach that furnished empirical significance levels would be better than a Bonferroni adjustment.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.151-165
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• 1999

This paper considers the trimmed least squares estimator of the autoregression parameter in the unstable AR(1) model: X\ulcorner=ØX\ulcorner+$\varepsilon$\ulcorner, where $\varepsilon$\ulcorner are iid random variables with mean 0 and variance $\sigma$$^2$> 0, and Ø is the real number with │Ø│=1. The trimmed least squares estimator for Ø is defined in analogy of that of Welsh(1987). The limiting distribution of the trimmed least squares estimator is derived under certain regularity conditions.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.531-541
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• 1997

The asymptotic distribution of residual autocorrelation functions from a generalized p-order random coefficient autoregressive process (GRCA(p)) is derived. To this end, we first describe the GRCA(p) models and then consider the normalised residuals after fitting the model. This result can be applied to the residual analysis for the diagonostic purpose.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.233-244
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• 2000

Confidence intervals for survival function give useful information about the lifetime distribution. In this paper we develop Edgeworkth expansions as approximation to the true and bootstrap distributions of normalized nonparametric maximum likelihood estimator of survival function in the Koziol-Green model and then use these results to show that the bootstrap approximations have second order accuracy.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.719-728
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• 1999

This paper discusses an optimal burn-in procedure to minimize total costs based on the assumption that the failure rate pattern follows a bimodal mixed Weibull distribution. The procedure will consider warranty period as a factor of the total expected burn-in cost. A cost model is formulated to find the optimal burn-in time that minimizes the expected burn-in cost. Conditional reliability for warranty period will be discussed. An illustrative example is included to show how to use the cost model in prctice.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.95-102
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• 2008

This paper presents an appropriate approach for modeling noise in Computed Radiography(CR) images of high strength materials. The approach is specifically designed for types of noise with the statistical and nonlinear properties. CR images Ere degraded even before they are encoded by computer process. Various types of noise often contribute to contaminate radiography image, although they are detected on digitalization. Quantum noise, which is Poisson distributed, is a shot noise, but the photon distribution on Image Plate(IP) of CR system is not always Poisson process. The statistical properties are relative and case-dependant due to its material characteristics. The usual assumption of a distribution of Poisson, binomial and Gaussian statistics are considered. Nonlinear effect is also represented in the process of statistical noise model. It leads to estimate the noise variance in regions from high to low intensity, specifying analytical model. The analysis approach is tested on a database of steel tube step-wedge CR images. The results are available for the comparative parameter studies which measure noise coherence, distribution, signal/noise ratios(SNR) and nonlinear interpolation.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.129-148
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• 2007

Frailty estimates from the proportional hazards frailty model often lead us to conjecture the heterogeneity in frailty such that the variance of the frailty varies over different covariate groups (e.g. male group versus female group). For such systematic heterogeneity in frailty, we consider a regression model for the variance components in the proportional hazards frailty model, denoted by the MLFM. However, in many cases, the observed data do not show any statistically significant preference between the homogeneous frailty model and the heterogeneous frailty model. In this paper, we propose a Bayesian model averaging procedure with the reversible jump Markov chain Monte Carlo which selects the appropriate model automatically. The resulting regression coefficient estimate ignores the model uncertainty from the frailty distribution in view of Bayesian model averaging (Hoeting et al., 1999). Finally, the proposed model and the estimation procedure are illustrated through the analysis of the kidney infection data in McGilchrist and Aisbett (1991) and a simulation study is implemented.

• Journal of applied mathematics & informatics
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• v.40 no.1_2
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• pp.1-13
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• 2022

This article introduces a new distribution called length-biased powered inverse Rayleigh distribution. Some of its statistical properties are derived. Maximum likelihood procedure is applied to report the point and interval estimations of all model parameters. The proposed distribution is also applied to two real data sets for illustrative purposes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.2 s.54
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• pp.115-121
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• 2009

The paper presents a signal-based damage detection algorithm of ARX model using dynamic acceleration data. An ARX model correlates acceleration data measured at two locations in a structure by considering those two sets of data as input and output signals. For detecting damage, the error between the measured data and the predicted response from the defined ARX model is computed in time and used for a statistical evaluation. A normal distribution function from the error in time is constructed and its statistical characteristic values are used for the evaluation of damage. By comparing the normal distribution functions before and after damage, three different types of damage indices are proposed. The efficiency and limitation of the proposed algorithm with the statistical evaluation of damage indices have been examined and discussed through laboratory experiments.