• IEIE Transactions on Smart Processing and Computing
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• v.3 no.1
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• pp.35-39
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• 2014

This paper proposes a Modified Minimum Mean Square Error (M-MMSE) estimator for an Orthogonal Frequency Division Multiplexing (OFDM) System over fast fading Rayleigh channel. The proposed M-MMSE estimator considered the effects of the efficient placement of pilots based on the channel energy distribution. The pilot symbols were placed in a non-linear manner according to the density of the channel energy. Comparative analysis of the MMSE estimator for a comb-type pilot arrangement and M-MMSE estimator for the proposed pilot insertion scheme revealed significant performance improvement of the M-MMSE estimator over the MMSE estimator.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.743-752
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• 2002

The estimation of the response curve is the important problem in the quantal bioassay. When we estimate the response curve, we determine the design points in advance of the experiment. Then naturally we have a question of which design would be optimal. As a response curve estimator, locally weighted quasi-likelihood estimator has several more appealing features than the traditional nonparametric estimators. The optimal design density for the locally weighted quasi-likelihood estimator is derived and its ability both in theoretical and in empirical point of view are investigated.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.146-148
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• 1995

A CFAR algorithm for weibull clutter is discussed. The Maximum a posteriori(MAP) estimator for two parameters(skewness and scale) of the weibull clutter is proposed, assuming the probability density function of skewness parameter is known. And proposed MAP estimator is compared with the Maximum likelihood(ML) estimator. Using this MAP estimator, we can design CFAR detector which is shown to have smaller CFAR loss than ML CFAR detector by the statistical simulation method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.1
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• pp.75-82
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• 1991

The objective of this paper is to investigate the optimal Robust estimation and scale estimator that could be used to treat the gross errors in a self calibrating bundle adjustment. In order to test the variability in performance of the different weighting schemes in accurately detecting gross error, five robust estimation methods and three types of scale estimators were used. And also, two difference control point patterns(high density control, sparse density control) and three types of gross errors(4$\sigma o$, 20$\sigma o$, 50$\sigma o$) were used for comparison analysis. As a result, Anscombe's robust estimation produced the best results in accuracy among the robust estimation methods considered. when considering the scale estimator about control point patterns, It can be seen that Type II scale estimator provided the best accuracy in high density control pattern. On the other hand, In the case of sparse density control pattern, Type III scale estimator showed the best results in accuracy. Therefore it is expected to apply to robustified bundle adjustment using the optimal scale estimator which can be used for eliminating the gross error in precise structure analysis.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.357-367
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• 2001

Huber의 M-추정함수의 형태는 조율상수가 주어질 때 비로소 그 형태가 결정된다. 조율상수를 커널밀도함수추정량의 평활계수를 이용하여 구하여 보았고, 모의실험을 통해 기존에 상요되는 조율상수들과 그 성능을 비교하여 보았다. 그 결과 새로운 방법에 의해 구해진 조율상수가 기존의 조율상수를 사용하는 경우 보다 모의실험을 통해 얻은 추정치의 분산이 작게되는 경우가 있음을 알았다.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.235-241
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• 1996

The ordinary least squares estimator of the disturbance variance in the linear regression model with stationary errors is shown to be asymptotically unbiased when the error process has a spectral density bounded from the above and away from zero. Such error processes cover a broad class of stationary processes, including ARMA processes.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.219-225
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• 2012

We derive approximate maximum likelihood estimators of two parameters in a weighted exponential distribution, and derive the density function for the ratio Y=(X+Y) of two independent weighted exponential random variables X and Y, and then observe the skewness of the ratio density.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.145-154
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• 2020

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.371-383
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• 2000

Lindsay (1994) and Basu et al (1997) show that another density-based distance called the negative exponential disparity (NED) is an excellent competitor to the Hellinger distance (HD) in generating an asymptotically fully efficient and robust estimator. Bhattacharya and Basu (1996) consider estimation of the locations of several normal populations when an order relation between them is known to be true. They empirically show that the robust HD based weighted likelihood estimators compare favorably with the M-estimators based on Huber's $\psi$ function, the Gastworth estimator, and the trimmed mean estimator. In this paper we investigate the performance of the weighted likelihood estimator based on the NED as a robust alternative relative to that based on the HD. The NED based estimator is found to be quite competitive in the settings considered by Bhattacharya and Basu.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.729-748
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• 1999

In this paper we present combined estimator of capture-recapture and line transect model using bivariate detection function and detection probability according to objects being in cluster population. Here bivariate detection function use distance and cluster size. The simulation shows that combined estimator approaches the more true value the larger size parameter. Therefore this estimator using the bivariate detection function is more efficient in estimate the population size and density by size parameter.