• The Korean Journal of Applied Statistics
• /
• v.17 no.3
• /
• pp.519-533
• /
• 2004

In this paper, we suggest quasi-likelihood estimation (QLE) method and its robust version in estimating spatial dependence modelled through variogram used for spatial data modelling. We compare the statistical characteristics of the estimators with other popular least squares estimators of parameters for variogram model by simulation study. The QLE method for estimating spatial dependence has the advantages that it does not need the concept of lags commonly required for least squares estimation methods as well as its statistical superiority. The QLE method also shows the statistical superiority to the other methods for the tested Gaussian and non-Gaussian spatial processes.

• The Korean Journal of Applied Statistics
• /
• v.17 no.3
• /
• pp.475-488
• /
• 2004

Several robust regression estimators are compared under contamination. Symmetric and asymmetric contamination schemes are used to measure the variance and MSE of regression estimators. Under asymmetric contamination depth-based regression estimator, especially projection based regression estimator(rcent) outperforms the rest and under symmetric contamination HBR performs relatively well.

• 한국데이터정보과학회:학술대회논문집
• /
• 2004.04a
• /
• pp.197-201
• /
• 2004

We describe a new edge detector based on the robust rank-order (RRO) test which is a useful alternative to Wilcoxon test, using $r{\times}r$ window for detecting edges of all possible orientations in noisy images. Some experiments of statistical edge detectors based on the Wilcoxon test and T test with our RRO detector are carried out on synthetic and real images corrupted by both Gaussian and impulse noise. We also implement these edge detectors using Java on the Web.

• Journal of Korean Society for Quality Management
• /
• v.27 no.2
• /
• pp.112-125
• /
• 1999

In a robust regression model, it is typically assumed that the errors are normally distributed. However, what if the error distribution is deviated from the normality and the response variables are not completely observable due to censoring? For complete data, Kim and Lai(1998) suggested a new adaptive M-estimator with an asymptotically efficient score function. The adaptive M-estimator is based on using B-splines to estimate the score function and simple cross validation to determine the knots of the B-splines, which are a modified version of Kun( 1992). We herein extend this method to right-censored data and study how well the adaptive M-estimator performs for various error distributions and censoring rates. Some impressive simulation results are shown.

• MALSORI
• /
• no.63
• /
• pp.153-170
• /
• 2007

It is well known that when there is an acoustic mismatch between the speech obtained during training and testing, the accuracy of speaker verification systems drastically deteriorates. This paper presents the use of MFCCs' histogram enhancement technique in order to improve the robustness of a speaker verification system. The technique transforms the features extracted from speech within an utterance such that their statistics conform to reference distributions. The reference distributions proposed in this paper are uniform distribution and beta distribution. The transformation modifies the contrast of MFCCs' histogram so that the performance of a speaker verification system is improved both in the clean training and testing environment and in the clean training and noisy testing environment.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.49 no.4
• /
• pp.219-225
• /
• 2000

This paper presents the shape optimization considering the uncertainty of design variable to find robust optimal solution that has insensitive performance to its change of design variable. Stochastic finite element method (SFEM) is used to treat input data as stochastic variables. It is method that the potential values are series form for the expectation and small variation. Using correlation function of their variables, the statistics of output obtained form the input data distributed. From this, design considering uncertainty of design variables.

• The Korean Journal of Applied Statistics
• /
• v.6 no.2
• /
• pp.303-309
• /
• 1993

In this paper we have defined two approaches to be error-robust when the precise form of error-structure is unknown. An experiment is optimal by the first criterion if it maximizes the minimum effciency over all candidates of error structure and is optimal by the second if it maximizes the minimum average of the efficiency over all candidates of error structure. In order to appreciate the basic implications of each design criterion, these approaches are applied to two different experimental situations, D- and heteroscedastic G-optimalities.

• Korean Journal of Oriental Medicine
• /
• v.12 no.2 s.17
• /
• pp.103-113
• /
• 2006

Objectives we intended to know how much did it relate with the results between the instruments of diagnosis by using methods of three factors - QSCCII, PSSC(Phonetic System for Sasang Constitution)-2004, and body measurement which are usually used in diagnosing the Sasang Constitution in clinics Methods We diagnosed Sasang constitution through QSCCII, PSSC(Phonetic System for Sasang Constitution)-2004, Body measurement as a dignosis factors and we used Kappa coefficient to estimate simularity between diagnosis factors, and SPSS 12.0K to analyze data Results and conchusions 1. The orders of agreement statistics are different in the currency of Sasang Constitution diagnosis, Soeum-in was highest and Taeum-in lowest in the the fricency of Sasang Conctitution Diagnosis in the QSCCII, Soeum-in was highest Soyang-in lowest in the PSSC and Taeum-in highest, Soyang-in lowest in the body measurement so, we analogized incorrection in Sasang Constitution Diagnosis 2. Among 443 subjects, 156 (35.3%) had same dignosis in three Sasang Constitution factors. It means agreement statistics among factors of diagnosis are very low, so it is absolutely nessessary to research connection among those, especially Soyang-in part 3. Totally, it is not robust to apply these factors on Sasang Constitution diagnosis, especially agreement statistics between two kinds of Sasang Constitution diagnosis as $0.358{\sim}0.380$. However, we can have a possibility the more we use Sasang Constitution dignosis factors, the higher the agreement statistics is, through the ascending of agreement statistics as $0.526{\sim}0.592$, among three kinds of Sasang Constitution diagnosis To evaluate accuracy of Sasang Constitution diagnosis, it is nessessary to collect data from the subjects who are dignosed through the evidences such as herb medicine, disease and normal symption observation, etc. Using these data, we have to evaluate correction of seperated Sasang Constitution diagnosis methods and to connect those.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.6
• /
• pp.25-35
• /
• 1997

Most of speech analysis methods developed up to date are based on second order statistics, and one of the biggest drawback of these methods is that they show dramatical performance degradation in noisy environments. On the contrary, the methods using higher order statistics(HOS), which has the property of suppressing Gaussian noise, enable robust feature extraction in noisy environments. In this paper we propose a text-independent speaker identification system using higher order statistics and compare its performance with that using the conventional second-order-statistics-based method in both white and colored noise environments. The proposed speaker identification system is based on the vector quantization approach, and employs HOS-based voiced/unvoiced detector in order to extract feature parameters for voiced speech only, which has non-Gaussian distribution and is known to contain most of speaker-specific characteristics. Experimental results using 50 speaker's database show that higher-order-statistics-based method gives a better identificaiton performance than the conventional second-order-statistics-based method in noisy environments.

• The Korean Journal of Applied Statistics
• /
• v.34 no.3
• /
• pp.309-327
• /
• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.