• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.155-162
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• 2017

In this paper we propose influence measures for two basic goodness-of-fit statistics, the coefficient of determination $R^2$ and test statistic F in the linear regression model using the deletion method. Some useful lemmas are provided. We also express the influence measures in terms of basic building blocks such as residual, leverage, and deviation that showed them as increasing function of residuals and a decreasing function of deviation. Further, the proposed measure reduces computational burden from O(n) to O(1). As illustrative examples, we applied the proposed measures to the stackloss data sets. We verified that deletion of one or few influential observations may result in big change in $R^2$ and F-statistic.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.1-12
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• 2004

In this paper, we consider choosing the appropriate covariance structure for analyzing repeated measures data with three repeat factors from a study of blood pressure data, which is collected from the local residents of Yangpyeong, Gyeonggi-do (2001) and fitted linear mixed models to find the significant covariates on outcome variable(Blood Pressure)

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.845-852
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• 2010

The outcomes of counts commonly occur in the area of disease mapping for mortality rates or disease rates. A Poisson distribution is usually assumed as a model of disease rates in conjunction with a gamma prior. The small area typically refers to a small geographical area or demographic group for which very little information is available from the sample surveys. Under this situation the model-based estimation is very popular, in which the auxiliary variables from various administrative sources are used. The empirical Bayes estimator under Poissongamma model has been considered with its accuracy measures. An accuracy measure using a bootstrap samples adjust the underestimation incurred by the posterior variance as an estimator of true mean squared error. We explain the suggested method through a practical dataset of hitters in baseball games. We also perform a Monte Carlo study to compare the accuracy measures of mean squared error.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.49-63
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• 2023

A lot of studies on the summary measures of predictive strength of categorical response models consider the likelihood ratio index (LRI), also known as the McFadden-R², a better option than many other measures. We propose a simple modification of the LRI that adjusts for the effect of the number of response categories on the measure and that also rescales its values, mimicking an underlying latent measure. The modified measure is applicable to both binary and ordinal response models fitted by maximum likelihood. Results from simulation studies and a real data example on the olfactory perception of boar taint show that the proposed measure outperforms most of the widely used goodness-of-fit measures for binary and ordinal models. The proposed R² interestingly proves quite invariant to an increasing number of response categories of an ordinal model.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.1-19
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• 2021

Heavy tailed distributions are useful for modeling actuarial and financial risk management problems. Actuaries often search for finding distributions that provide the best fit to heavy tailed data sets. In the present work, we introduce a new class of heavy tailed distributions of a special sub-model of the proposed family, called a new extended alpha power transformed Weibull distribution, useful for modeling heavy tailed data sets. Mathematical properties along with certain characterizations of the proposed distribution are presented. Maximum likelihood estimates of the model parameters are obtained. A simulation study is provided to evaluate the performance of the maximum likelihood estimators. Actuarial measures such as Value at Risk and Tail Value at Risk are also calculated. Further, a simulation study based on the actuarial measures is done. Finally, an application of the proposed model to a heavy tailed data set is presented. The proposed distribution is compared with some well-known (i) two-parameter models, (ii) three-parameter models and (iii) four-parameter models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.1
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• pp.15-23
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• 2004

Westfeld analyzed a sequential LSB embedding steganography effectively through the $\chi$$^2$statistical test which measures the frequencies of PoVs(pairs of values). Fridrich also proposed another statistical analysis, so-called RS steganalysis by which the embedding message rate can be estimated. This method is based on the partition of pixels as three groups; Regular, Singular, Unusable groups. In this paper, we propose a new steganographic scheme which preserves the above two statistics. The proposed scheme embeds the secret message in the innocent image by randomly adding one to real pixel value or subtracting one from it, then adjusts the statistical measures to equal those of the original image.

• Bulletin of the Korean Mathematical Society
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• v.55 no.2
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• pp.561-572
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• 2018

In this paper we consider the concept of statistical causality in continuous time between flows of information, represented by filtrations. Then we relate the given concept of causality to the equivalent change of measure that plays an important role in mathematical finance. We give necessary and sufficient conditions, in terms of statistical causality, for extremality of measure in the set of martingale measures. Also, we have considered the extremality of measure which involves the stopping time and the stopped processes, and obtained similar results. Finally, we show that the concept of unique equivalent martingale measure is strongly connected to the given concept of causality and apply this result to the continuous market model.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.2
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• pp.1-6
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• 1982

This paper reviews and describes some applications of perturbation theory in the practical analysis of linear systems which involve random parameters. Statistical measures of the system outputs are derived in terms of statistical measures of the system parameters and inputs (i.e., in the way of perturbed linear operator equations). Perturbed state transition matrix is also derived. With simple first-order and second-order linear system models, we compare the accuracy of perturbation results with the exact ones. And the convergence of perturbation series is also investigated.

• Journal of the Korea Safety Management & Science
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• v.9 no.5
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• pp.43-48
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• 2007

This paper is to present linkage parameter to integrate statistical models and physical models for accelerated life test. Statistical models represent the relationship of probability distribution and life. Physical models show the relationship of life and stress. Moreover, this study proposes the four steps for construction of integrated models for accelerated life test using linkage parameter. Finally, this paper develops new integrated models such as extreme value distribution-general Eyring, linearly increasing failure rate function-general Eyring, etc., and estimates various reliability measures.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.475-485
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• 2015

Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretable compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern of the change.