• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.11
• /
• pp.9-15
• /
• 1997

The Nakagami m-distribution is used to model different fading environments and shown to fit experimental resutls more accurately than other distributions. In this paper, the probability of symbol error for M-ary QAM with square signal constellation in frequency-nonselective shlow Nakagami fading and additive white Gaussian noise is derived. When Nakagami fading index is integer, the derived resutls leads to the closed-form of a finite series.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.4
• /
• pp.899-910
• /
• 2004

In this paper we have proposed a new loss function, namely, non-linear exponential(NLINEX) loss function, which is quite asymmetric in nature. We obtained the Bayes estimator under exponential(LINEX) and squared error(SE) loss functions. Moreover, a numerical comparison among the Bayes estimators of power function distribution under SE, LINEX, and NLINEX loss function have been made.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2008.04a
• /
• pp.33-36
• /
• 2008

We introduce some properties for fuzzy binomial distributions with fuzzy valued probability. First we define fuzzy type I error and type II error for fuzzy relative frequency and agreement index. And we show that an fuzzy power function and fuzzy binomial frequency function for binomial proportion test.

• Communications for Statistical Applications and Methods
• /
• v.2 no.2
• /
• pp.13-19
• /
• 1995

This paper considers model robust accelerated life test plans for estimating the logmean or percentile of product lige which is exponentially distributed. A linear relationship between the log mean life and the stress is assumed as usual, while the true relationship is quadratic. Optimum plans are then obtained by minimizing asymptotic mean squared error of maximum likelihood estimator of the log mean life.

• Journal of the Korean Statistical Society
• /
• v.25 no.2
• /
• pp.265-276
• /
• 1996

The nonparametric deconvolution problems are studied to recover an unknown density when the data are contaminated with Gaussian error. We propose the estimator which is a linear combination of kernel type estimates of derivertives of the observed density function. We show that this estimator is consistent and also consider the properties of estimator at small sample by simulation.

• Journal of the Korean Data and Information Science Society
• /
• v.12 no.2
• /
• pp.35-45
• /
• 2001

We introduce a new percentile envelope for diagnosing supernormality in regression analysis. Furthermore, we compare this percentile envelope, which is much simpler and easier, with Atkinson's and Flack and Flores' envelopes. Using percentile envelope, we investigate characteristics of normal probability plots with envelope for error distributions when supernormality is occurred. We give cautions that test result for normality assumption of errors can be reached the wrong conclusion by supernormality.

• Proceedings of the Safety Management and Science Conference
• /
• 2000.11a
• /
• pp.217-234
• /
• 2000

Approximately normalized control charts, called Q charts, have been given Quesenberry(1991) for charting in process of short-run, job-shop, etc. We consider a Q chart with inspection error for job-shop floor under geometric distribution, which can be used for processes when a fraction nonconforming is very small. Our results would be applied for designing other control charts with inspection error.

• Management Science and Financial Engineering
• /
• v.9 no.1
• /
• pp.4-10
• /
• 2003

We introduce a new estimator of the uncertainty of a jackknife estimate of standard error: the jack-knife-after-jackknife (JAJ). Using Monte Carlo simulation, we assess the accuracy of the JAJ in a variety of settings defined by statistic of interest, data distribution, and sample size. For comparison, we also assess the accuracy of the jackknife-after-bootstrap (JAB) estimate of the uncertainty of a bootstrap standard error. We conclude that the JAJ provides a useful new supplement to Tukey's jackknife, and the combination of jackknife and JAJ provides a useful alternative to the combination of bootstrap and JAB.

• Management Science and Financial Engineering
• /
• v.9 no.1
• /
• pp.1-10
• /
• 2003

We introduce a new estimator of the uncertainty of a jackknife estimate of standard error: the jack-knife-after-jackknife (JAJ). Using Monte Carlo simulation, we assess the accuracy of the JAJ in a variety of settings defined by statistic of interest, data distribution, and sample size. For comparison, we also assess the accuracy of the jackknife-after-bootstrap (JAB) estimate of the uncertainty of a bootstrap standard error. We conclude that the JAJ provides a useful new supplement to Tukey's jackknife, and the combination of jackknife and JAJ provides a useful alternative to the combination of bootstrap and JAB.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.13 no.1
• /
• pp.12-18
• /
• 1988

It is found that the estimation pdf of the real intraframe DPCM prediction error by $x^3$ goodness-of-fit test for the images is nearer gamma distribution that laplacian. Also the new pdf estimation method by NMAE is proposed and applied to the pdf adaptive DPCM system.