• Journal of the Korean Statistical Society
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• v.21 no.1
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• pp.27-34
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• 1992

This paper provides a method of obtaining a lower bound on the probability of correct selection for a two-stage selection procedure. The resulting lower bound sharpens that by Tamhane and Bechhofer (1979) for the normal means problem with a common known variance. The design constants associated with the lower bound are computed and the results of the performance comparisons are given.

• Journal of the Korean Statistical Society
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• v.18 no.1
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• pp.26-37
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• 1989

When we select the t best out of k populations in the indifference zone formulation, a lower confidence bound on the probability of a correct selection is derived for families with monotone likelihood ratio. The result is applied to the normal means problem when the variance is common, and to the normal variances problem. Tables to implement the confidence bound for the normal variances problem are provided.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.439-449
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• 2020

Meta-analysis provides a way of integrating several independent studies of interest. Since small studies with statistically significant results are more likely to be published, publication bias, which is a special case of selection bias, often occurs in meta analysis. Conditional likelihood and weighted estimating equation have been proposed to deal with publication bias, but they require to specify a correct selection probability model. In contrast, the pairwise pseudolikelihood approach can correct publication bias without fully specifying the correct selection probability model, but its performance in meta-analysis was not investigated. In this paper, we perform a numerical study about whether the pairwise pseudolikelihood approach is effective for solving publication bias arising from typical meta-analysis settings.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.129-142
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• 2017

In this paper we consider the problem of the model selection/discrimination among three different positively skewed lifetime distributions. Lindley, Weibull, and gamma distributions have been used to effectively analyze positively skewed lifetime data. This paper assesses how much closer the Lindley distribution gets to Weibull and gamma distributions. We consider three techniques that involve the likelihood ratio test, asymptotic likelihood ratio test, and minimum Kolmogorov distance as optimality criteria to diagnose the appropriate fitting model among the three distributions for a given data set. Monte Carlo simulation study is performed for computing the probability of correct selection based on the considered optimality criteria among these families of distributions for various choices of sample sizes and shape parameters. It is observed that overall, the Lindley distribution is closer to Weibull distribution in the sense of likelihood ratio and Kolmogorov criteria. A real data set is presented and analyzed for illustrative purposes.

• Journal of the Korean Statistical Society
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• v.32 no.2
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• pp.151-162
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• 2003

In this paper, we propose two selection procedures for selecting populations better than a control population. The bestness is defined in terms of location parameter. One of the procedures is based on two-sample linear rank statistics whereas the other one is based on a comparatively simple statistic, and is useful when testing time is expensive so that an early termination of an experiment is desirable. The proposed selection procedures are seen to be strongly monotone. Performance of the proposed procedures is assessed through simulation study.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.602-617
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• 2014

The performance of a cognitive radio network (CRN) solely depends on how precisely the secondary users can sense the presence or absence of primary users. The incorporation of a spatial false alarm makes deriving the probability of a correct decision a cumbersome task. Previous literature performed this task for the case of a received signal under a Normal probability density function case. In this paper we enhance the previous work, including the impact of carrier frequency, the gain of antennas on both sides, and antenna heights so as to observe the robustness against noise and interference and to make the correct decision of detection. Three small scale fading channels: Rayleigh, Normal, and Weibull were considered to get the real scenario of a CRN in an urban area. The incorporation of a maximal-ratio combining and selection combing with a variation of the number of received antennas have also been studied in order to achieve the correct decision of spectral sensing, so as to serve the cognitive users. Finally, we applied the above concept to a traffic model of the CRN, which we based on a two-dimensional state transition chain.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.41-51
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• 1994

We consider nonparametric procedures for the selection and ranking problems. Tukey's generalized lambda distribution is condidered as the distribution for the score function because the distribution can approximate many well-known contionuous distributions. Also we compare these procedures in terms of efficiency, defined by the ratio of a probability of a correct selection divided by the expected selected subset size.

• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.299-309
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• 2003

Let ${\pi}_1,...,{\pi}_{k}$k($\geq$2) independent logistic populations such that the cumulative distribution function (cdf) of an observation from the population ${\pi}_{i}$ is $$F_{i}\;=\; {\frac{1}{1+exp{-\pi(x-{\mu}_{i})/(\sigma\sqrt{3})}}},\;$\mid$x$\mid$<\;{\infty}$$ where ${\mu}_{i}(-{\infty}\; < \; {\mu}_{i}\; <\; {\infty}$ is unknown location mean and ${\delta}^2$ is known variance, i ＝ 1,..., $textsc{k}$. Let ${\mu}_{[k]}$ be the largest of all ${\mu}$'s and the population ${\pi}_{i}$ is defined to be 'good' if ${\mu}_{i}\;{\geq}\;{\mu}_{[k]}\;-\;{\delta}_1$, where ${\delta}_1\;>\;0$, i = 1,...,$textsc{k}$. A selection procedure based on sample median is proposed to select a subset of $textsc{k}$ logistic populations which includes all the good populations with probability at least $P^{*}$(a preassigned value). Simultaneous confidence intervals for the differences of location parameters, which can be derived with the help of proposed procedures, are discussed. If a population with location parameter ${\mu}_{i}\;<\;{\mu}_{[k]}\;-\;{\delta}_2({\delta}_2\;>{\delta}_1)$, i ＝ 1,...,$textsc{k}$ is considered 'bad', a selection procedure is proposed so that the probability of either selecting a bad population or omitting a good population is at most 1­ $P^{*}$.

• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.183-205
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• 2007

An investigation was undertaken of the optimal discriminant model for predicting the likelihood of insolvency in advance for medium-sized firms based on the technology evaluation. The explanatory variables included in the discriminant model were selected by both factor analysis and discriminant analysis using stepwise selection method. Five explanatory variables were selected in factor analysis in terms of explanatory ratio and communality. Six explanatory variables were selected in stepwise discriminant analysis. The effectiveness of linear discriminant model and logistic discriminant model were assessed by the criteria of the critical probability and correct classification rate. Result showed that both model had similar correct classification rate and the linear discriminant model was preferred to the logistic discriminant model in terms of criteria of the critical probability In case of the linear discriminant model with critical probability of 0.5, the total-group correct classification rate was 70.4% and correct classification rates of insolvent and solvent groups were 73.4% and 69.5% respectively. Correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify the present sample. However, the actual correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify a future observation. Unfortunately, the correct classification rate underestimates the actual correct classification rate because the data set used to estimate the discriminant function is also used to evaluate them. The cross-validation method were used to estimate the bias of the correct classification rate. According to the results the estimated bias were 2.9% and the predicted actual correct classification rate was 67.5%. And a threshold value is set to establish an in-doubt category. Results of linear discriminant model can be applied for the technology financing banks to evaluate the possibility of insolvency and give the ranking of the firms applied.

• Journal of Korean Academy of Nursing
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• v.41 no.6
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• pp.750-757
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• 2011

Purpose: Cost-benefit analysis is one of the most commonly used economic evaluation methods, which helps to inform the economic value of a program to decision makers. However, the selection of a correct benefit estimation method remains critical for accurate cost-benefit analysis. This paper compared benefit estimations among three different benefit estimation models. Methods: Data from community-based chronic hypertension management programs in a city in South Korea were used. Three different benefit estimation methods were compared. The first was a standard deterministic estimation model; second, a repeated-measures deterministic estimation model; and third, a transitional probability estimation model. Results: The estimated net benefit of the three different methods were $1,273.01, $-3,749.42, and $-5,122.55 respectively. Conclusion: The transitional probability estimation model showed the most correct and realistic benefit estimation, as it traced possible paths of changing status between time points and it accounted for both positive and negative benefits.