• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.159-171
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• 2001

Shapiro and Wilk (1972) developed a test for exponentiality with origin and scale unknown. The procedure consists of comparing the generalized least squares estimate of scale with the estimate of scale given by the sample variance. However the test statistic is inconsistent ; that is, the power of the test will not approach 1 as the sample size increases. Hence we give a test based on the ratio of two asymptotically efficient estimates of scale. We also have conducted a power study to compare the test procedures, using Monte Carlo samples from a wide range of alternatives. It is found that the suggested statistics have higher power for the alternatives with the coefficient of variation greater that or equal to 1.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.299-309
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• 2001

In PET(Positron Emission Tomography), it is necessary to use transmission scan data in order to estimate the attenuation map. Recently, there are several empirical studies in which one might be able to estimate attenuation map and activity distribution simultaneously with emissive sinogram alone without transmission scan. However, their algorithms are based on the model in which does not include the background counts term, and so is unrealistic. If the background counts component has been included in the model, their algorithm would introduce non-monotonic reconstruction algorithm which results in vain in practice. in this paper, we develop a monotonic and parallelizable algorithm for simultaneous reconstruction of both characteristics and present the validity through some simulations.

• Journal of the Korean Statistical Society
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• 제4권1호
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• pp.79-87
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• 1975

However, the mechanism for producing revised estimate is the principal means of varying the resulting precisions of estimate. Therefore, a scheduling system including physician's revision should be checked by a computer simulation to evaluate possible gains to admissions scheduling accruing from the use of these estimates. The ability to accurately predict bed occupancy has long been an objective of hospital management. If the one were able to anticipate bed accupancy, then the one could more accurately plan for bed needs, schedule personnel, allocate service and supply. We may conclude that even though the Markov chain analysis would not lead to ready-made answers for the scheduling system of elective patients, it can provide the more detailed and systematic knowledgy for the solutions on the weekly base as well as the foundations for long run planning in relative sense.

• 응용통계연구
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• 제25권4호
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• pp.633-640
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• 2012

Biological methods are described for the assay of certain substances and preparations whose potency cannot be adequately assured by chemical or physical analysis. The principle applied through these assays is of a comparison with a standard preparation to determine how much of the examined substance produces the same biological effects as a given quantity (the Unit) of the standard preparation. In these dilution assays, to estimate the relative potencies of the unknown preparations to the standard preparations, it is necessary to compare dose-response relationships of standard and unknown preparations. The dose-response relationship in the dilution assay is non-linear and sigmoid when a wide range of doses is applied. The parallel line model (applied to the dose region with the steepest slope) is used to estimate the relative potency. In this paper, the statistical theory in the parallel line model is explained with an application to a dilution assay data. The parallel line method is implemented in a SAS program and is available at the author's homepage(http://cafe.daum.net/go.analysis).

• Communications for Statistical Applications and Methods
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• 제18권2호
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• pp.179-187
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• 2011

제 1상 입상시험의 주된 목적은 신약의 최대허용용량(Maximum tolerated dose; MTD)의 추정이다. 본 논문에서는 실험으로 얻어진 Dose-toxicity data를 S자 모형에 적합 시켜 MTD를 추정하는 방법을 제안하였다. 멈춤 규칙(stopping rule)에 의해 MTD가 결정되는 방법과 미리 정해진 표본수에서 실험을 종료하고 MTD를 추정하는 기존의 추정방법을 본 논문에서 제안한 방법과 모의실험을 통하여 비교하였다.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.112-121
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• 1995

Let $p_1$ and $p_2$ be the proportions of two populations. To test the hypothesis $H_0 : p_1 = p_2$, we usually use the $x^2$ statistic, the large sample binomial statistic Z, and the Generalized Likelihood Ratio statistic-2log $\lambda$developed based on different mathematical rationale, respectively. Since testing the above hypothesis is equivalent to testing whether two populations follow the common Bernoulli distribution, one may also test the hypothesis by comparing 1 with the ratio of each density estimate and the hypothesized common density estimate, called comparison density, which was devised by Parzen(1988). We show that the above traditional test statistics ate actually estimating the measure of distance between the true densities and the common density under $H_0$ by representing them with the comparison density.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.583-595
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• 2004

For the Korean Economically Active Population Survey(EAPS), we consider the composite estimator based on logistic regression model to estimate the unemployment rate for small areas(Si/Gun). Also, small area estimation technique based on hierarchical generalized linear model is proposed to include the random effect which reflect the characteristic of the small areas. The proposed estimation techniques are applied to real domestic data which is from the Korean EAPS of Choongbuk. The MSE of these estimators are estimated by Jackknife method, and the efficiencies of small area estimators are evaluated by the RRMSE. As a result, the composite estimator based on logistic model is much more efficient than others and it turns out that the composite estimator can produce the reliable estimates under the current EAPS system.

• Journal of the Korean Statistical Society
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• 제32권1호
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• pp.33-46
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• 2003

The continual reassessment method (CRM) provides a Bayesian estimation of the maximum tolerated dose (MTD) in phase I clinical trials. The CRM has been proposed as an alternative design of the standard design. The CRM has been modified to improve practical feasibility and, recently, the likelihood approach CRM has been proposed. In this paper we investigate the consistency and asymptotic normality of the modified likelihood approach CRM in which the maximum likelihood estimate is used instead of the posterior mean. Small-sample properties of the consistency is examined using complete enumeration. Both the asymptotic results and their small-sample properties show that the modified CRML outperforms the standard design.

• Communications for Statistical Applications and Methods
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• 제23권6호
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• pp.555-562
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• 2016

Interval censored data often occur in an observational study where the subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are available. There are several methods to analyze interval censored failure time data (Sun, 2006). However, in the presence of competing risks, few methods have been suggested to estimate covariate effect on interval censored competing risk data. A sub-distribution hazard model is a commonly used regression model because it has one-to-one correspondence with a cumulative incidence function. Alternatively, Klein and Andersen (2005) proposed a pseudo-value approach that directly uses the cumulative incidence function. In this paper, we consider an extension of the pseudo-value approach into the interval censored data to estimate regression coefficients. The pseudo-values generated from the estimated cumulative incidence function then become response variables in a generalized estimating equation. Simulation studies show that the suggested method performs well in several situations and an HIV-AIDS cohort study is analyzed as a real data example.