• The Journal of the Korea Contents Association
• /
• v.10 no.12
• /
• pp.34-42
• /
• 2010

The DOA(direction of arrival), which is based on parametric and nonparametric estimation algorithm, and adaptive beamforming algorithm for mobile communication environments are researched and analyzed. In parametric estimation algorithm, eigenvalues of the signal component and the noise component are obtained from correlation matrix of received signal by array antenna and power spectrum of the received signal is discriminated from them. Otherwise, in nonparametric estimation algorithm, we minimize a regularized objective function for finding a estimate of the signal energy as a function of angle, using nonquadratic norm which leads to supper resolution and noise suppression. And then, DOA is estimated by the signal and noise spatial steering vector, and adaptive beam-forming pattern is improved by weight vectors obtained from the spatial vector. Therefore, the improved directional estimation algorithm with regularizing sparsity constraints offers super-resolution and noise suppression compared to other algorithms.

• Journal of information and communication convergence engineering
• /
• v.7 no.4
• /
• pp.501-506
• /
• 2009

In the H.264/ AVC video coding standard, the intra-prediction coding with various block sizes offers a considerably high improvement in coding efficiency compared to previous standards. In order to achieve this, H.264/AVC uses the Rate-distortion optimization (RDO) technique to select the best intraprediction mode for a macroblock, and it brings about the drastic increase of the computation complexity of H.264 encoder. To reduce the computation complexity and stabilize the coding performance on visual quality, this paper proposed a fast intra-prediction mode decision algorithm using non-parametric thresholds and simplified directional masks. The use of nonparametric thresholds makes the intra-coding performance not be dependent on types of video sequences and simplified directional masks reduces the compuation loads needed by the calculation of local edge information. Experiment results show that the proposed algorithm is able to reduce more than 55% of the whole encoding time with a negligible loss in PSNR and bitrates and provides the stable performance regardless types of video sequences.

• Communications for Statistical Applications and Methods
• /
• v.26 no.1
• /
• pp.57-67
• /
• 2019

Income distribution is a major concern in economic theory. In regional economics, it is often of interest to compare income distributions in different regions. Traditional methods often compare the income inequality of different regions by assuming parametric forms of the income distributions, or using summary statistics like the Gini coefficient. In this paper, we propose a nonparametric procedure to test for heterogeneity in income distributions among different regions, and a K-means clustering procedure for clustering income distributions based on energy distance. In simulation studies, it is shown that the energy distance based method has competitive results with other common methods in hypothesis testing, and the energy distance based clustering method performs well in the clustering problem. The proposed approaches are applied in analyzing data from China Health and Nutrition Survey 2011. The results indicate that there are significant differences among income distributions of the 12 provinces in the dataset. After applying a 4-means clustering algorithm, we obtained the clustering results of the income distributions in the 12 provinces.

• Journal of information and communication convergence engineering
• /
• v.7 no.2
• /
• pp.193-198
• /
• 2009

The H.264/ AVC video coding standard supports the intra prediction with various block sizes for luma component and a 8x8 block size for chroma components. This new feature of H.264/AVC offers a considerably higher improvement in coding efficiency compared to previous compression standards. In order to achieve this, H.264/AVC uses the Rate-distortion optimization (RDO) technique to select the best intra prediction mode for each block size, and it brings about the drastic increase of the computation complexity of H.264 encoder. In this paper, a fast block size decision algorithm is proposed to reduce the computation complexity of the intra prediction in H.264/AVC. The proposed algorithm computes the smoothness based on AC and DC coefficient energy for macroblocks and compares with the nonparametric criteria which is determined by considering information on neighbor blocks already reconstructed, so that deciding the best probable block size for the intra prediction. Also, the use of non-parametric criteria makes the performance of intra-coding not be dependent on types of video sequences. The experimental results show that the proposed algorithm is able to reduce up to 30% of the whole encoding time with a negligible loss in PSNR and bitrates and provides the stable performance regardless types of video sequences.

• Communications for Statistical Applications and Methods
• /
• v.29 no.6
• /
• pp.641-653
• /
• 2022

Penalized least squares methods are important tools to simultaneously select variables and estimate parameters in linear regression. The penalized maximum likelihood can also be used for the same purpose assuming that the error distribution falls in a certain parametric family of distributions. However, the use of a certain parametric family can suffer a misspecification problem which undermines the estimation accuracy. To give sufficient flexibility to the error distribution, we propose to use the symmetric log-concave error distribution with LASSO penalty. A feasible algorithm to estimate both nonparametric and parametric components in the proposed model is provided. Some numerical studies are also presented showing that the proposed method produces more efficient estimators than some existing methods with similar variable selection performance.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.283-287
• /
• 2010

Stochastic simulation of hydrologic data has been widely developed for several decades. However, despite the several advances made in literature still a number of limitations and problems remain. In the current study, some stochastic simulation approaches tackling some of the existing problems are discussed. The presented models are based on nonparametric techniques such as block bootstrapping, and K-nearest neighbor resampling (KNNR), and kernel density estimate (KDE). Three different types of the presented stochastic simulation models are (1) Pilot Gamma Kernel estimate with KNNR (a single site case) and (2) Enhanced Nonparametric Disaggregation with Genetic Algorithm (a disaggregation case). We applied these models to one of the most challenging and critical river basins in USA, the Colorado River. These models are embedded into the hydrological software package, Pros and cons of the models compared with existing models are presented through basic statistics and drought and storage-related statistics.

• Journal of the Korean Statistical Society
• /
• v.31 no.4
• /
• pp.509-518
• /
• 2002

This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is put on the baseline cumulative intensity function and a usual parametric prior distribution is given to the regression parameter. Also we allow heterogeneity among the intensity processes in different subjects by using unobserved random frailty components. Gibbs sampling approach with the Metropolis-Hastings algorithm is used to explore the posterior distributions. Finally, the results are applied to a real data set.

• ETRI Journal
• /
• v.34 no.5
• /
• pp.771-774
• /
• 2012

Typical channel allocation algorithms for secondary users do not include processes to reduce the frequency of switching from one channel to another caused by random interruptions by primary users, which results in high packet drops and delays. In this letter, with the purpose of decreasing the number of switches made between channels, we propose a nonparametric channel allocation algorithm that uses robust kernel density estimation to effectively schedule idle channel resources. Experiment and simulation results demonstrate that the proposed algorithm outperforms both random and parametric channel allocation algorithms in terms of throughput and packet drops.

• Journal of Industrial Technology
• /
• v.43 no.1
• /
• pp.57-62
• /
• 2023

In this review, we introduce the non-parametric Bayesian filtering algorithm known as the point-mass filter (PMF) and discuss recent studies related to it. PMF realizes Bayesian filtering by placing a deterministic grid on the state space and calculating the probability density at each grid point. PMF is known for its robustness and high accuracy compared to other nonparametric Bayesian filtering algorithms due to its uniform sampling. However, a drawback of PMF is its inherently high computational complexity in the prediction phase. In this review, we aim to understand the principles of the PMF algorithm and the reasons for the high computational complexity, and summarize recent research efforts to overcome this challenge. We hope that this review contributes to encouraging the consideration of PMF applications for various systems.

• Communications for Statistical Applications and Methods
• /
• v.11 no.1
• /
• pp.161-168
• /
• 2004

A single-index model is useful in fields which employ multidimensional regression models. Many methods have been developed in parametric and nonparametric approaches. In this paper, posterior inference is considered and a wavelet series is thought of as a function approximated to a true function in the single-index model. The posterior inference needs a prior distribution for each parameter estimated. A prior distribution of each coefficient of the wavelet series is proposed as a hierarchical distribution. A direction $\beta$ is assumed with a unit vector and affects estimate of the true function. Because of the constraint of the direction, a transformation, a spherical polar coordinate $\theta$, of the direction is required. Since the posterior distribution of the direction is unknown, we apply a Metropolis-Hastings algorithm to generate random samples of the direction. Through a Monte Carlo simulation we investigate estimates of the true function and the direction.