• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.951-959
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• 2014

Recently Hazelton and Turlach (2009) proposed a weighted kernel density estimator for the deconvolution problem. In the case of Gaussian kernels and measurement error, they argued that the weighted kernel density estimator is a competitive estimator over the classical deconvolution kernel estimator. In this paper we consider weighted kernel density estimators when sample observations are contaminated by double exponentially distributed errors. The performance of the weighted kernel density estimators is compared over the classical deconvolution kernel estimator and the kernel density estimator based on the support vector regression method by means of a simulation study. The weighted density estimator with the Gaussian kernel shows numerical instability in practical implementation of optimization function. However the weighted density estimates with the double exponential kernel has very similar patterns to the classical kernel density estimates in the simulations, but the shape is less satisfactory than the classical kernel density estimator with the Gaussian kernel.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.99-111
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• 1995

It is shown that the adaptive kernel methods can potentially produce superior density estimates to the fixed one. In using the adaptive estimates, problems pertain to the initial choice of the estimate can be solved by iteration. Also, simultaneous recommended for variety of distributions. Some data-based method for the choice of the parameters are suggested based on simulation study.

• Water Engineering Research
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• v.2 no.1
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• pp.1-10
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• 2001

The frequency analyses for the precipitation data in Korea were performed. We used daily maximum series, monthly maximum series, and annual series. For nonparametric frequency analyses, variable kernel estimators were used. Nonparametric methods do not require assumptions about the underlying populations from which the data are obtained. Therefore, they are better suited for multimodal distributions with the advantage of not requiring a distributional assumption. In order to compare their performance with parametric distributions, we considered several probability density functions. They are Gamma, Gumbel, Log-normal, Log-Pearson type III, Exponential, Generalized logistic, Generalized Pareto, and Wakeby distributions. The variable kernel estimates are comparable and are in the middle of the range of the parametric estimates. The variable kernel estimates show a very small probability in extrapolation beyond the largest observed data in the sample. However, the log-variable kernel estimates remedied these defects with the log-transformed data.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.939-946
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• 2008

In this paper the support vector method is presented for the probability density function estimation when the sample observations are contaminated with random noise. The performance of the procedure is compared to kernel density estimates by the simulation study.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.443-451
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• 2005

The studied in this paper is a new algorithm for searching the maximum likelihood estimate(MLE) in which probability density function is not explicitly expressed. Newton-Raphson's root-finding routine and a nonlinear numerical optimization algorithm with constraint (so-called feasible sequential quadratic programming) are used. This algorithm is applied to the Wakeby distribution which is importantly used in hydrology and water resource research for analysis of extreme rainfall. The performance comparison between maximum likelihood estimates and method of L-moment estimates (L-ME) is studied by Monte-carlo simulation. The recommended methods are L-ME for up to 300 observations and MLE for over the sample size, respectively. Methods for speeding up the algorithm and for computing variances of estimates are discussed.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.34-36
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• 2003

In boreal conifers in China's Northeast area, maximum latewood density (MXD) of tree-ring varies in response to growing season temperature. Forest net productivity can be estimated using the Normalized-difference Vegetation Index (NDVI) calculated from satellite sensor data. MXD from the Mohe site in this area was compared with estimates of NPP for 1982-1999 produced by the NDVI model, which was established based on the relationship of leaf area index (LAI) and NDVI. The result shows that the MXD series correlated significantly with the NDVI model estimates series, suggesting that MXD appeared to be an appropriate index for productivity or canopy growth in region where forest productivity is strongly temperature-related.

• Proceedings of the Acoustical Society of Korea Conference
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• 1984.12a
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• pp.100-106
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• 1984

Traditional methods for estimating the location of underwater target, i.e. the triangulation method and the wavefront curvature method, have been utilized. The location of a target is defined by the range and the bearing, which estimates can be obtained by evaluating the time delay between neighboring sensors. Many components of error occur in estimating the target range, among which the error due to the fluctuation of heading angle is outstanding. In this paper, the wavefront curvature method was used. We considered the error due to the heading fluctuation as the $\beta$-density process, from which we analized the range estimates with $\beta$-density function exist in some finite limits, and its mean value and variation are depicted as a function of true range and heading fluctuation. Given heading angles and sensor separation, maximum estimated heading errors are presented as a function of true range.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.57 no.4
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• pp.351-364
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• 2021

In this study, the vertical distribution of giant jellyfish analyzed echo counting method and such survey methods as sighting and trawl were used to compare its density estimates. In May and July 2021, surveys were conducted in the East China Sea and the coastal waters of Korea. As a result, Nemopilema nomurai were evenly distributed in all water layers in East China Sea in May and July. When considered the correlation by each survey methods, it is possible to identify jellyfish in the surface area by sighting method and using sampling net; however, it has a low correlation with acoustic estimates due to marine environmental factor such as weather condition, wind and atmospheric pressure. Therefore, the result can be utilized by basic data when estimating jellyfish's distribution patterns and density estimates from each survey methods hereafter.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.349-360
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• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.