• Journal of the Korean Statistical Society
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• v.13 no.2
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• pp.87-106
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• 1984

It ${X_t}$ is a sequence of independent identically distributed normal random variables, then the conditional probability density of $X_1, X_2, \cdots, X_n$ given the first p+1 sample autocovariances converges to the maximum entropy probability density satisfying the corresponding covariance constraints as the length of the sample sequence tends to infinity. This establishes that the maximum entropy probability density and the associated Gaussian autoregressive process arise naturally as the answers of conditional limit problems.

• Journal of the military operations research society of Korea
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• v.11 no.2
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• pp.69-82
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• 1985

In this study, we consider the simultaneous estimation of the parameters of the distribution of p independent Poisson random variables using the weighted loss function. The relation between the estimation under the weighted loss function and the case when more than one observation is taken from some population is studied. We derive an estimator which dominates Tsui and Press's estimator when certain conditions hold. We also derive an estimator which dominates the maximum likelihood estimator(MLE) under the various loss function. The risk performances of proposed estimators are compared to that of MLE by computer simulation.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.2
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• pp.243-247
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• 2014

Let {$X_n$, $n{\geq}1$} be a sequence of i.i.d. random variables with absolutely continuous cumulative distribution function(cdf) F(x) and the corresponding probability density function(pdf) f(x). In this paper, we give characterizations of Pareto and Weibull distribution by considering conditional expectations of record values.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.11-18
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• 1994

Sample sizes for experiments concerned with inference on R=Pr(X

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.187-199
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• 2003

We deal with the problem of testing a sequence of independent normal random variables with constant, known or unknown, variance for no change in mean versus alternatives with a single change-point. Various tests based on the likelihood ratio and recursive residuals, score statistics and cusums are studied. Proposed tests are modified version of Buckley's cusum statistics. A comparison study of various change-point test statistics is done by Monte Carlo simulation with S-plus software.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.162-165
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• 2003

Differential learning relies on the differentiated values of nodes, whereas the conventional learning depends on the values themselves of nodes. In this paper, I elucidate the differential learning in the framework maximum likelihood learning of linear generative model with latent variables obeying random walk. I apply the idea of differential learning to the problem independent component analysis(ICA), which leads to differential ICA. Algorithm derivation using the natural gradient and local stability analysis are provided. Usefulness of the algorithm is emphasized in the case of blind separation of temporally correlated sources and is demonstrated through a simple numerical example.

• Journal of Korean Society for Quality Management
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• v.14 no.1
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• pp.2-10
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• 1986

In this paper t he minimum variance unbiased, maximum likelihood and empirical estimators of the probability $P_r$ ($X_1<Y<X_2$) are obtained, where $X_1$, $X_2$ and Y are mutually independent exponential random variables. Comparison of estimators is discussed in the last section for illustraition.

• Bulletin of the Korean Mathematical Society
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• v.57 no.5
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• pp.1115-1126
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• 2020

The main purpose of this paper is to establish the rates of convergence in weak limit theorems for normalized geometric sums of independent identically distributed random variables via Zolotarev's probability metric.

• Journal of the Korean Statistical Society
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• v.18 no.1
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• pp.38-45
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• 1989

A procedure is considered to the problem of testing whether there exist changes in location at possibly two points in a sequence of independent random variables which are successively drawn from normal population. A test statistics based on modified likelihood ratio is proposed and its asymptotic null distribution is derived through the stochastic process representation. A small sample power comparison is made by Monte Carlo method.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.151-166
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• 1994

Let $X_1, \cdot, X_p$ be p independent random variables, where each $X_i$ has a distribution belonging to one parameter discrete power series distribution. The problem is to simultaneously estimate the unknown parameters under an asymmetric loss. Several new classes of dominating estimators are obtained by solving certain difference inequality.