• Journal of the Korean Statistical Society
• /
• v.32 no.4
• /
• pp.411-423
• /
• 2003

In this paper we develop a method for calculating a probability that a particular generalized variance is the smallest of all the K multivariate normal generalized variances. The method gives a way of comparing K multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approach for the probability is intractable and thus a Bayesian method is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach. Necessary theory involved in the method and computation is provided.

• Journal of Korean Institute of Industrial Engineers
• /
• v.38 no.1
• /
• pp.25-30
• /
• 2012

Desirability approaches have been proposed to find an optimum of multiple response problem. The existing desirability approaches use either of mean or min of individual desirability in aggregation of multiple responses. However, in order to find an optimum having high mean and low dispersion among individual desirability, the dispersion needs to be simultaneously considered with its mean. This study proposes bias and variance (BV) method which aggregates bias (ideal target-mean) and variance of individual desirability in multiple response optimization. The proposed BV method was applied to an example to evaluate its usefulness by comparing with existing methods. Evaluation results showed that the solution of BV method was a fairly good compared with DS (Derringer and Suich, 1980) and KL (Kim and Lin, 2000) methods. The BV method can be utilized to multiple response surface problems when decision makers want to find an optimum having high mean and low variance among responses.

• Journal of Korean Institute of Industrial Engineers
• /
• v.19 no.1
• /
• pp.61-71
• /
• 1993

We focus on a way of combining the Monte Calro methods of antithetic variates and control variates to reduce the variance of the estimator of the mean response in a simulation experiment. Combined Method applies antithetic variates (partially) for driving approiate stochastic model components to reduce the vaiance of estimator and utilizes the correlations between the response and control variates. We obtain the variance of the estimator for the response analytically and compare Combined Method with control variates method. We explore the efficiency of this method in reducing the variance of the estimator through the port operations model. Combined Method shows a better performance in reducing the variance of estimator than methods of antithetic variates and control variates in the range from 6% to 8%. The marginal efficiency gain of this method is modest for the example considered. When the effective set of control variates is small, the marginal efficiency gain may increase. Though these results are from the limited experiments, Combined Method could profitably be applied to large-scale simulation models.

• Journal of the Korean Statistical Society
• /
• v.29 no.4
• /
• pp.385-394
• /
• 2000

Jackknife variance estimation based on adjusted imputed values when nonresponse is nonrandom and follow-up data are available for a subsample of nonrespondents is provided. Both hot-deck and ratio imputation method are considered as imputation method. The performance of the proposed variance estimator under nonrandom response mechanism is investigated through numerical simulation.

• Proceedings of the KSR Conference
• /
• 2002.05a
• /
• pp.567-571
• /
• 2002

In this study we reported the Smoke Density test method of interior panel of railroad passenger car and investigated the specific smoke density(Ds) by NBS smoke chamber to the variance of some test conditions. First we compared the result of Ds from ISO 5659-2 with that from ASTM E662 for same phenol FRP Secondary studied the Ds value to the variance of resin type and to the variance of specimen shape.

• Proceedings of the Korean Association for Survey Research Conference
• /
• 2001.04a
• /
• pp.59-71
• /
• 2001

We investigate design-based variance estimation methods of homogeneous linear estimator for population total under stratified multi-stage sampling. One method is unbiasedly estimating the first stage variance and the second stage variance separately in each stratum. And another is sub-sampling method that estimating the first stage variance only by using sub-sample selected from the second stage sample so that resulting estimator is unbiased for the total variance. The first is useful when the second stage unbiased estimator is available and the second is when the second stage variance is not estimable. For each case, we proposed a form of non-negative unbiased variance estimator. We expect the proposed variance estimation methods can be effectively used for many practical surveys.

• Survey Research
• /
• v.2 no.1
• /
• pp.59-71
• /
• 2001

We investigate design-based variance estimation methods of homogeneous linear estimator for population total under stratified multi-stage sampling. One method is unbiasedly estimating the first stage variance and the second stage variance separately in each stratum. And another is sub-sampling method that estimating the first stage variance only by using sub-sample selected from the second stage sample so that resulting estimator is unbiased for the total variance. The first is useful when the second stage unbiased estimator is available and the second is when the second stage variance is not estimable. For each case, we proposed a form of non-negative unbiased variance estimator. We expect the proposed variance estimation methods can be effectively used for many practical surveys.

• Communications for Statistical Applications and Methods
• /
• v.12 no.2
• /
• pp.365-379
• /
• 2005

Balanced Repeated Replication(BRR) is widely used to estimate the variance of linear or nonlinear estimators from complex sampling surveys. Most of survey data sets include imputed missing values and treat the imputed values as observed data. But applying the standard BRR variance estimation formula for imputed data does not produce valid variance estimators. Shao, Chen and Chen(1998) proposed an adjusted BRR method by adjusting the imputed data to produce more accurate variance estimators. In this paper, another adjusted BRR method is proposed with examples of real data.

• Nuclear Engineering and Technology
• /
• v.55 no.11
• /
• pp.4228-4237
• /
• 2023

The Improved Deterministic Truncation of Monte Carlo (iDTMC) is a powerful acceleration and variance reduction scheme in the Monte Carlo analysis. The concept of the iDTMC method and correlated sampling-based real variance estimation are briefly introduced. Moreover, the application of the iterative scheme to the correlated sampling is discussed. The iDTMC method is utilized in a 3-dimensional small modular reactor (SMR) model problem. The real variances of burnup-dependent criticality and power distribution are evaluated and compared with the ones obtained from 30 independent iDTMC calculations. The impact of the inactive cycles on the correlated sampling is also evaluated to investigate the consistency of the correlated sample scheme. In addition, numerical performances and sensitivity analysis on the real variance estimation are performed in view of the figure of merit of the iDTMC method. The numerical results show that the correlated sampling accurately estimates the real variances with high computational efficiencies.

• KIEAE Journal
• /
• v.16 no.2
• /
• pp.17-22
• /
• 2016

Purpose: Sensitivity analysis offers a good guideline for designing energy conscious buildings, which is fitted to a specific building configuration. Sensitivity analysis is, however, still too expensive to be a part of regular design process. The One-at-a-time (OAT) is the most common and simplest sensitivity analysis method. This study aims to propose a reasonable ground that the OAT can be an alternative method for the variance-based method in some early design scenarios, while the variance-based method is known adequate for dealing with nonlinear response and the effect of interactions between input variables, which are most cases in building energy simulations. Method: A test model representing the early design phase is built in the DOE2 energy simulations. Then sensitivity ranks between the OAT and the Variance-based methods are compared at three U.S. sites. Result: Parameters in the upper rank by the OAT do not much differ from those by the Main effect index. Considering design practices that designers would chose the most energy saving design option first, this rank similarity between two methods seems to be acceptable in the early design phase.