• 대한수학회지
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• 제51권3호
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• pp.609-633
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• 2014

From the ordinary notion of uniformly strong mixing for a sequence of random variables, a new concept called conditionally uniformly strong mixing is proposed and the relation between uniformly strong mixing and conditionally uniformly strong mixing is answered by examples, that is, uniformly strong mixing neither implies nor is implied by conditionally uniformly strong mixing. A couple of equivalent definitions and some of basic properties of conditionally uniformly strong mixing random variables are derived, and several conditional covariance inequalities are obtained. By means of these properties and conditional covariance inequalities, a conditional central limit theorem stated in terms of conditional characteristic functions is established, which is a conditional version of the earlier result under the non-conditional case.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.959-966
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• 2000

A dimensionality assessment procedure DETECT uses the property of being near zero of conditional covariances as an indication of unidimensionality .This study provides the convergent properties to zero of conditional covariances when the dta is unidimensional, with which DETECT extends its theoretical grounds.

• Communications for Statistical Applications and Methods
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• 제18권6호
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• pp.799-808
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• 2011

Let {${\Omega}$, $\mathcal{F}$, P} be a probability space and {$X_n{\mid}n{\geq}1$} be a sequence of random variables defined on it. A finite sequence of random variables {$X_i{\mid}1{\leq}i{\leq}n$} is a conditional associated given $\mathcal{F}$ if for any coordinate-wise nondecreasing functions f and g defined on $R^n$, $Cov^{\mathcal{F}}$ (f($X_1$, ${\ldots}$, $X_n$), g($X_1$, ${\ldots}$, $X_n$)) ${\geq}$ 0 a.s. whenever the conditional covariance exists. We obtain the H$\grave{a}$jek-R$\grave{e}$nyi-type inequality for conditional associated random variables. In addition, we establish the strong law of large numbers, the three series theorem, integrability of supremum, and a strong growth rate for $\mathcal{F}$-associated random variables.

• 전자공학회논문지SC
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• 제39권2호
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• pp.166-175
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• 2002

이 논문에서는 표적 추적에 사용되는 PSN(Probabilistic Strongest Neighbor) 필터의 추적 성능을 예측한다. PSN 필터는 가장 강한 신호 크기를 가진 측정이 표적이외의 것으로부터 발생할 수 있다는 사건을 충분히 고려하기 때문에, 추적 성능에서 뿐만 아니라, 계산량 측면에서도 PDA(Probabilistic Data association) 필터보다 뛰어나다고 알려져 있다. 추적필터의 추정오차 공분산행렬(covariance matrix)은 추적의 성능을 결정하는 성능지수(performance index)로 널리 사용된다. PSN 필터의 추정오차 공분산행렬은 측정 데이터의 함수로써, 측정 데이터와 무관하게 추적기의 성능을 표현하기 위해서 HYCA(HYbrid Conditional Average)방법을 이용하여 추정오차 공분산행렬의 기대값에 대한 식을 제시하였다. 수치실험을 통하여 이 논문에서 제시한 성능 예측이 타당함을 보인다.

• 재무관리연구
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• 제26권3호
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• pp.199-226
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• 2009

본 논문은 우리나라 주식시장과 외환시장의 기대 수익률과 조건부 변동성간의 시계열적 관계를 2요인 자본자산가격결정모형(two-factor ICAPM)을 이용하여 실증 분석하였다. 주가와 환율의 조건부 분산은 GARCH 모형과 비대칭성을 반영한 GJR(1993) 모형으로 추정하였으며, 주가와 환율과의 조건부 공분산은 Bollerslev(1990)의 일정 상관관계(CCC) 모형과 Engle(2002)의 동태적 조건부상관관계(DCC) 모형을 이용하여 추정하였다. 실증 분석모형은 MGARCH-M 모형을 사용하였으며, 추정방법은 준최우추정법(QMLE)을 사용하였다. 실증 분석결과 외환위기 이후에 주식시장의 기대 수익률은 주가의 분산에 대해, 그리고 환율과의 공분산에 대해 유의한 음(-)의 관계를 갖는 것으로 나타났다. 그러나 외환시장에서 기대 수익률은 조건부 분산과 조건부 공분산에 대해 유의하지 않은 것으로 나타났다. 조건부 분산의 추정에서는 GJR 모형이 GARCH 모형에 비해 더 적합한 것으로 나타났다. 그리고 DCC 모형이 CCC 모형에 비해 설명력이 더 높은 것으로 나타났다. 본 논문의 분석결과는 주식시장에서 환율 변동이 위험 요인으로 작용하고 있기 때문에 포트폴리오 구성이나 위험 관리 등에서 환율 변동을 고려할 필요가 있고, 변수들간의 상관관계는 시변하는 모형을 사용할 필요가 있음을 시사한다.

• Journal of the Korean Statistical Society
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• 제13권2호
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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.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.19-30
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• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.

• 대한수학회지
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• 제33권4호
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• pp.763-775
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• 1996

For $Q = [0,S] \times [0,T]$ let C(Q) denote Yeh-Wiener space, i.e., the space of all real-valued continuous functions x(s,t) on Q such that x(0,t) = x(s,0) = 0 for every (s,t) in Q. Yeh [10] defined a Gaussian measure $m_y$ on C(Q) (later modified in [13]) such that as a stochastic process ${x(s,t), (s,t) \epsilon Q}$ has mean $E[x(s,t)] = \smallint_{C(Q)} x(s,t)m_y(dx) = 0$ and covariance $E[x(s,t)x(u,\upsilon)] = min{s,u} min{t,\upsilon}$. Let $C_\omega \equiv C[0,T]$ denote the standard Wiener space on [0,T] with Wiener measure $m_\omega$. Yeh [12] introduced the concept of the conditional Wiener integral of F given X, E(F$\mid$X), and for case X(x) = x(T) obtained some very useful results including a Kac-Feynman integral equation.

• Journal of the Korean Statistical Society
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• 제33권1호
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• pp.107-128
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• 2004

Bivariate Laplace distributions for which both marginal distributions and Laplace are discussed. Three kinds of bivariate Laplace distributions which are extended bivariate exponential distributions of Gumbel (1960) are introduced in this paper. These symmetrical distributions are compared with asymmetrical distributions of Kotz et al. (2000). Their probability density functions, cumulative distribution functions are derived. Conditional skewnesses and kurtoses are also defined. Their correlation coefficients are calculated and compared with others. We proposed bivariate random vector generating methods whose distributions are bivariate Laplace. With sample means and medians obtained from generated random vectors, variance and covariance matrices of means and medians are calculated and discussed with those of bivariate normal distribution.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.189-200
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• 2013

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation whether the training samples include missing values or not. We consider the conditional bootstrap confidence intervals for classification error rate when a block of observation is missing.