• Journal of the Korean Statistical Society
• /
• v.20 no.2
• /
• pp.118-138
• /
• 1991

Given a random sample of size N(unknown) with density f(x $\theta$), suppose that only n observations which lie outside a region R are recorded. On the basis of n observations, the Bayes estimators of $\theta$ and N are considered and their asymptotic expansions are developed to compare their second order asymptotic properties with those of the maximum likelihood estimators and the Bayes modal estimators. Corrections to bias and median bias of these estimators are made. An example is given to illustrate the results obtained.

• Journal of applied mathematics & informatics
• /
• v.26 no.5_6
• /
• pp.933-943
• /
• 2008

Spectral analysis of a strictly stationary r-vector valued time series is considered under the assumption that some of the observations are missed due to some random failure. Statistical properties and asymptotic moments are derived. Asymptotic normality is discussed.

• Publications of The Korean Astronomical Society
• /
• v.17 no.1
• /
• pp.11-14
• /
• 2002

Our examination of the relations of spherically symmetric accretion on a massive point object to viscous drag, neglecting gas pressure and using self-similar transformation, shows the behaviors of the asymptotic solutions? in the regions near to and far from the center. The viscosity reduces the free-fall velocity by the factor $(1\;+\;\zeta) ^{-1}$, and causes flattening in the density distribution. Therefore, the viscosity leads to the reduction of the mass accretion rate.

• Communications for Statistical Applications and Methods
• /
• v.8 no.2
• /
• pp.483-497
• /
• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Journal of the Korean Statistical Society
• /
• v.24 no.1
• /
• pp.149-160
• /
• 1995

We consider the problem of optimal adaptive estiamtion of the euclidean parameter vector $\theta$ of the univariate non-linerar autogressive time series model ${X_t}$ which is defined by the following system of stochastic difference equations ; $X_t = \sum^p_{i=1} \theta_i \cdot T_i(X_{t-1})+e_t, t=1, \cdots, n$, where $\theta$ is the unknown parameter vector which descrives the deterministic dynamics of the stochastic process ${X_t}$ and ${e_t}$ is the sequence of white noises with unknown density $f(\cdot)$. Under some general growth conditions on $T_i(\cdot)$ which guarantee ergodicity of the process, we construct a sequence of adaptive estimatros which is locally asymptotic minimax (LAM) efficient and also attains the least possible covariance matrix among all regular estimators for arbitrary symmetric density.

• Journal of the Korean Mathematical Society
• /
• v.39 no.5
• /
• pp.731-743
• /
• 2002

Let M be a Riemannian manifold with asymptotically non-negative curvature. We study the asymptotic behavior of the energy densities of a harmonic map and an exponentially harmonic function on M. We prove that the energy density of a bounded harmonic map vanishes at infinity when the target is a Cartan-Hadamard manifold. Also we prove that the energy density of a bounded exponentially harmonic function vanishes at infinity.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.16 no.4
• /
• pp.225-234
• /
• 2012

We present two high-order potential flow models for the evolution of the interface in the Rayleigh-Taylor instability in two dimensions. One is the source-flow model and the other is the Layzer-type model which is based on an analytic potential. The late-time asymptotic solution of the source-flow model for arbitrary density jump is obtained. The asymptotic bubble curvature is found to be independent to the density jump of the fluids. We also give the time-evolution solutions of the two models by integrating equations numerically. We show that the two high-order models give more accurate solutions for the bubble evolution than their low-order models, but the solution of the source-flow model agrees much better with the numerical solution than the Layzer model.

• Journal of the Korean Statistical Society
• /
• v.34 no.1
• /
• pp.49-60
• /
• 2005

In this paper we introduce a nonparametric method for estimating the probability density function of detection distances in line transect sampling. The estimator is obtained using a frequency histogram density estimation method. The asymptotic properties of the proposed estimator are derived and compared with those of the kernel estimator under the assumption that the data collected satisfy the shoulder condition. We found that the asymptotic mean square error (AMSE) of the two estimators have about the same convergence rate. The formula for the optimal histogram bin width is derived which minimizes AMSE. Moreover, the performances of the corresponding k-nearest-neighbor estimators are studied through simulation techniques. In the absence of our knowledge whether the shoulder condition is valid or not a new semi-parametric model is suggested to fit the line transect data. The performances of the proposed two estimators are studied and compared with some existing nonparametric and semiparametric estimators using simulation techniques. The results demonstrate the superiority of the new estimators in most cases considered.

• Journal of the Korean Mathematical Society
• /
• v.53 no.4
• /
• pp.929-967
• /
• 2016

Let p(t, x) be the fundamental solution to the problem $${\partial}^{\alpha}_tu=-(-{\Delta})^{\beta}u,\;{\alpha}{\in}(0,2),\;{\beta}{\in}(0,{\infty})$$. If ${\alpha},{\beta}{\in}(0,1)$, then the kernel p(t, x) becomes the transition density of a Levy process delayed by an inverse subordinator. In this paper we provide the asymptotic behaviors and sharp upper bounds of p(t, x) and its space and time fractional derivatives $$D^n_x(-{\Delta}_x)^{\gamma}D^{\sigma}_tI^{\delta}_tp(t,x),\;{\forall}n{\in}{\mathbb{Z}}_+,\;{\gamma}{\in}[0,{\beta}],\;{\sigma},{\delta}{\in}[0,{\infty})$$, where $D^n_x$ x is a partial derivative of order n with respect to x, $(-{\Delta}_x)^{\gamma}$ is a fractional Laplace operator and $D^{\sigma}_t$ and $I^{\delta}_t$ are Riemann-Liouville fractional derivative and integral respectively.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.23 no.6
• /
• pp.1454-1460
• /
• 1998

In this ppaer, we propose a new detector based on the median-shift sign, and then investigate its asymptotic and finite sample-size performance. We call it the median-shift sign (MSS) detector, which is an extension of the classical sign detector. First, we consider the asymptotic opti$$\mu$ median shift values and their characteristics. Next, we consider the asymptotic relative efficiency of the MSS detectors. we then consider the problem of detecting known signals in noise of known probability density function, and the problem of detecting known signals when only partial information is available on the noise.