• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.367-379
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• 2004

This article deals with the nonparametric analysis of longitudinal data when there exist possible correlations among repeated measurements for a given subject. We consider a quasi-likelihood regression model where a transformation of the regression function through a link function is linear in time-varying coefficients. We investigate the local polynomial approach to estimate the time-varying coefficients, and derive the asymptotic distribution of the estimators in this quasi-likelihood context. A real data set is analyzed as an illustrative example.

• Journal of the Korean Statistical Society
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• 제25권3호
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• pp.381-391
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• 1996

In the analysis of contingency table with ordered categories, the relationship between odds for adjacent categories has received con-siderable interest. We consider likelihood ratio tests of independence against an order restriction on odds in 2 $\times$ k contingency tables.

• Journal of Communications and Networks
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• 제14권5호
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.13-22
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• 2000

We consider local likelihood method with a smoothed version of the model density in stead of an original model density. For simplicity a model is assumed as the log-linear density then we were able to show that the proposed local density estimator is less affected by changes among observations but its bias increases little bit more than that of the currently used local density estimator. Hence if we use the existing method and the proposed method in a proper way we would derive the local density estimator fitting the data in a better way.

• 응용통계연구
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• 제19권3호
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• pp.587-598
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• 2006

본 논문에서는 마이크로어레이 (microarray) 자료에 판별분석을 적용 시 나타나는 고차원 및 소표본 문제의 해결방법으로서 직교요인을 새로운 특징변수로 사용한 비모수적 국소선형 로지스틱 판별분석을 제안한다. 제안된 방법은 국소우도에 기반한 것으로서 다범주 판별분석에 적용될 수 있으며, 고려된 직교인자는 주성분 요인, 부분최소제곱 요인, 인자분석 요인 등이다. 대표적인 두 가지 실제 마이크로어레이 자료에 적용한 결과 직교요인들 중에서 부분최소제곱 요인을 특징변수로 사용한 경우 고전적인 통계적 판별분석보다 향상된 분류 능력을 나타내고 있음을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.153-165
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• 2003

This paper considers the likelihood ratio test statistic based on nonlinear regression quantiles estimators in order to test of hypothesis about the regression parameter $\theta_o$ and derives asymptotic distribution of proposed test statistic under the null hypothesis and a sequence of local alternative hypothesis. The paper also investigates asymptotic relative efficiency of the proposed test to the test based on the least squares estimators or the least absolute deviation estimators and gives some examples to illustrate the application of the main result.

• 대한수학회지
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• 제47권2호
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• pp.385-407
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• 2010

To study the relationship between the levels of chemical pollutants and the number of daily total hospital admissions for respiratory diseases and to find the effect of temperature/relative humidity on the admission number, Wong et al. [17] introduced the varying-coefficient single-index model (VCSIM). As pointed out, it is a popular multivariate nonparametric fitting technique. However, the tests of the model have not been very well developed. In this paper, based on the estimators obtained by the local linear technique, the average method and the one-step back-fitting technique in the VCSIM, the generalized likelihood ratio (GLR) tests for varying-coefficient parts on the VCSIM are established. Under the null hypotheses the new proposed GLR tests follow the $\chi^2$-distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters, known as Wilks phenomenon. Simulations are conducted to evaluate the test procedure empirically. A real example is used to illustrate the performance of the testing approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.14-17
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• 1995

Various methods and techniques have been proposed for solving optimization problems; the methods have been applied to various practical problems. However the methods have demerits. The demerits which should be covered are, for example, falling into local minima, or, a slow convergence speed to optimal points. In this paper, Likelihood Search Method (L.S.M.) is proposed for searching for a global optimum systematically and effectively in a single framework, which is not a combination of different methods. The L.S.M. is a sort of a random search method (R.S.M.) and thus can get out of local minima. However exploitation of gradient information makes the L.S.M. superior in convergence speed to the commonly used R.S.M..

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.49-53
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• 2005

• Journal of the Korean Data and Information Science Society
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• 제20권1호
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• pp.1-9
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• 2009

회귀모형의 분산함수가 알려져 있지 않은 한 점에서 불연속이라 가정하자. Yu와 Jones (2004)는 음이 아닌 값을 취하는 분산함수를 실수 값을 취하도록 하기 위하여 로그 변환하였고, 변환된 로그분산함수를 국소다항적합으로 추정하였다. 로그분산함수의 국소다항적합을 이용하여, Huh (2008)는 분산함수의 불연속점의 추정하는 대신 로그분산함수의 불연속점을 추정하였다. 본 연구는 Huh의 점프의 크기 추정량의 점근분포를 이용하여 로그분산함수의 불연속점의 존재여부에 대한 가설검정을 제안하고, 제안한 방법에 대한 모의실험 결과를 제시하고자 한다.