• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.473-481
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• 2005

The testing problem for sphericity structure of the covariance matrix in a multivariate normal distribution is introduced when there is a sample with 2-step monotone missing data pattern. The maximum likelihood method is described to estimate the parameters on the basis of the sample. Using these estimates, the likelihood ratio criterion for testing sphericity is derived.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.187-198
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• 1994

We derive a new Cramer-Rao type lower bound for the reciprocal of the density height of the median-unbiased estimators which improves most of the previous lower bounds and is attainable under much weaker conditions. We also identify useful necessary and sufficient condition for the attainability of the lower bound which is considerably weaker than those for the mean-unbiased estimators. It is shown that these lower bounds are attained not only for the family of continuous distributions with monotone likelihood ratio (MLR) property but also for the location and scale families with strong unimodal property.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.503-515
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• 1998

When a neutral particle beam(NPB) aimed at the object and receive a small number of neutron signals at the detector, it follows approximately Poisson distribution. Under the four assumptions in the presence of errors and uncertainties for the Poisson parameters, an exact probability distribution of neutral particles have been derived. The probability distribution for the neutron signals received by a detector averaged over the three sources of errors is expressed as a four-dimensional integral of certain data. Two of the four integrals can be evaluated analytically and thereby the integral is reduced to a two-dimensional integral. The monotone likelihood ratio(MLR) property of the distribution is proved by using the Cauchy mean value theorem for the univariate distribution and multivariate distribution. Its MLR property can be used to find a criteria for the hypothesis testing problem related to the distribution.

• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.35-44
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• 1999

We derive a new upper bound of convolution type for the median-unbiased estimators with respect to an arbitrary unimodal utility functions. We also obtain the necessary and sufficient condition for the attainability of the information bound. Applications to general MLR(Monotone Likelihood Ratio) model and censored survival data re discussed as examples.

• Journal of Korean Society for Quality Management
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• v.26 no.4
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• pp.79-87
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• 1998

This paper considers the problem of testing a one-sided hypothesis under the generalized sampling plan which is defined by a sequence of independent Bernoulli trials. A certain lexicographic order is defined for the boundary points of the sampling plan. It is shown that the family of probability mass function defined on the boundary points has monotone likelihood ratio, and that the test function is uniformly most powerful.

• 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.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.166-175
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• 1995

When a neutral particle beam(NPB) aimed at the object and receive a small number of neutron signals at the detector without any errors, it obeys Poisson law. Under the two assumptions that neutral particle scattering distribution and aiming errors have a circular Gaussian distributions that neutral particle scattering distribution and aiming errors have a circular Gaussian distribution respectively, an exact probability distribution of neutral particles vecomes a Poisson-power function distribution. We study and prove some properties, such as limiting distribution, unimodality, stochastical ordering, computational recursion fornula, of this distribution. We also prove monotone likelihood ratio(MLR) property of this distribution. Its MLR property can be used to find a criteria for the hypothesis testing problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.967-973
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• 1995

A neutral particle beam(NPB) aimed at the object and receive a small number of neutron signals at the detector to estimate the mass of an object Since there is uncertainty about the location of the axis of the beam relative to the object, we could have aiming errors which may lead to incorrect information about the object. Under the two assumptions that neutral particle scattering distribution and aiming errors have a circular normal distribution respectively, we have derived an exact probability distribution of neutral particles. It becomes a Poison-power function distribution., We proved monotone likelihood ratio property of tlis distribution. This property can be used to find a criteria for the hypothesis testing problem.