• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.849-860
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• 1999

This paper addresses the issue of estimating regression function with errors in variables using wavelets. We adopt a nonparametric approach in assuming that the regression function has no specific parametric form, To account for errors in covariates deconvolution is involved in the construction of a new class of linear wavelet estimators. using the wavelet characterization of Besov spaces the question of regression estimation with Besov constraint can be reduced to a problem in a space of sequences. Rates of convergence are studied over Besov function classes $B_{spq}$ using $L_2$ error measure. It is shown that the rates of convergence depend on the smoothness s of the regression function and the decay rate of characteristic function of the contaminating error.

• Journal of the Korean Home Economics Association
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• v.32 no.3
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• pp.27-44
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• 1994

Despite the rapid economic growth since the 1960s the economic inequality has been exacerbated in Korea. This study analyzed the variables influencing the level of objective deprivation. For empirical analysis this study used the data on 602 households in the city of Inchon collected by the researcher through interviews. The major method used in this study was the four stepwise multiple regression. The findings were as follows : the residential class was the most critical variable in determining the level of deprivation. For the entire sample assets had stronger effect on the deprivation than nonasset income but two variables had different effects depending on residential class. For the poor residential class two variables had the effect These results imply that the household consumption in Korea shows remarkable difference according to residential class and that the inequality of wealth compared to that of nonasset income had much more serious effects.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.727-739
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• 2003

In this paper, we propose a generation method of regression classes for adaptation in the HM-Net (Hidden Markov Network) system. The MLLR (Maximum Likelihood Linear Regression) adaptation approach is applied to the HM-Net speech recognition system for expressing the characteristics of speaker effectively and the use of HM-Net in various tasks. For the state level sharing, the context domain state splitting of PDT-SSS (Phonetic Decision Tree-based Successive State Splitting) algorithm, which has the contextual and time domain clustering, is adopted. In each state of contextual domain, the desired phoneme classes are determined by splitting the context information (classes) including target speaker's speech data. The number of adaptation parameters, such as means and variances, is autonomously controlled by contextual domain state splitting of PDT-SSS, depending on the context information and the amount of adaptation utterances from a new speaker. The experiments are performed to verify the effectiveness of the proposed method on the KLE (The center for Korean Language Engineering) 452 data and YNU (Yeungnam Dniv) 200 data. The experimental results show that the accuracies of phone, word, and sentence recognition system increased by 34∼37%, 9%, and 20%, respectively, Compared with performance according to the length of adaptation utterances, the performance are also significantly improved even in short adaptation utterances. Therefore, we can argue that the proposed regression class method is well applied to HM-Net speech recognition system employing MLLR speaker adaptation.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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• pp.99-106
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• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.

• Imaging Science in Dentistry
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• v.46 no.2
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• pp.77-85
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• 2016

Purpose: To analyze the relative position of the mandibular foramina (MnFs) in patients diagnosed with skeletal class III malocclusion. Materials and Methods: Computed tomography (CT) images were collected from 85 patients. The vertical lengths of each anatomic point from the five horizontal planes passing through the MnF were measured at the coronoid process, sigmoid notch, condyle, and the gonion. The distance from the anterior ramus point to the posterior ramus point on the five horizontal planes was designated the anteroposterior horizontal distance of the ramus for each plane. The perpendicular distance from each anterior ramus point to each vertical plane through the MnF was designated the horizontal distance from the anterior ramus to the MnF. The horizontal and vertical positions were examined by regression analysis. Results: Regression analysis showed the heights of the coronoid process, sigmoid notch, and condyle for the five horizontal planes were significantly related to the height of the MnF, with the highest significance associated with the MnF-mandibular plane (coefficients of determination ($R^2$): 0.424, 0.597, and 0.604, respectively). The horizontal anteroposterior length of the ramus and the distance from the anterior ramus point to the MnF were significant by regression analysis. Conclusion: The relative position of the MnF was significantly related to the vertical heights of the sigmoid notch, coronoid process, and condyle as well as to the horizontal anteroposterior length of the ascending ramus. These findings should be clinically useful for patients with skeletal class III mandibular prognathism.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.289-301
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• 2011

In this paper an attempt has been made to classify Asia land cover considering climatic and vegetative characteristics. The sub-class clustering based on the 13 MODIS land cover classes (except water) over Asia was performed with the climate map and the NOVI derived from SPOT 5 VGT D10 data. The unsupervised classification for the sub-class clustering was performed in each land cover class, and total 74 clusters were determined over the study area. Via these clusters, the annual variations (from 1999 to 2007) of precipitation rate and temperature were analyzed as an example by a simple linear regression model. The various annual variations (negative or positive pattern) were represented for each cluster because of the various climate zones and NOVI annual cycles. Therefore, the detailed land cover map as the classification result by the sub-class clustering in this study can be useful information in modelling works for requiring the detailed climatic and vegetative information as a boundary condition.

• Journal of the Korean Society of Safety
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• v.38 no.5
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• pp.15-26
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• 2023

The relationship between the knowledge, attitude, and practice of the person in charge of designating a Class III facility was analyzed to improve its practice. As a field of knowledge, system knowledge and technical knowledge were considered, and attitudes were divided into cognitive, affective, and behavioral attitudes. A knowledge, attitude, and practice (KAP) survey was conducted, and the relationship among them was analyzed through correlation and regression analyses. The factors affecting the level of practice in designating the Class III facility were technical knowledge in the field of knowledge and cognitive and behavioral attitudes in the field of attitudes. Cognitive and behavioral attitudes were the two factors that most influenced the practice of designating a Class III facility. It is thought that the higher the level of cognitive and behavioral attitudes, the greater the ability to practice designating the Class III facility. The general characteristics of respondents influencing cognitive and behavioral attitudes were analyzed by safety inspection.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.131-141
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• 2003

In this paper, we prove that multi-variate fuzzy polynomials are universal approximators for multi-variate fuzzy functions which are the extension principle of continuous real-valued function under $T_W-based$ fuzzy arithmetic operations for a distance measure that Buckley et al.(1999) used. We also consider a class of fuzzy polynomial regression model. A mixed non-linear programming approach is used to derive the satisfying solution.

• Journal of the Korean Statistical Society
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• v.18 no.1
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• pp.46-61
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• 1989

This paper is concerned with the development of a nonparametric procedure for the statistical inference about the nonlinear regression parameters. A confidence region and a hypothesis testing procedure based on a class of signed linear rank statistics are proposed and the asymptotic distributions of the test statistic both under the null hypothesis and under a sequence of local alternatives are investigated. Some desirable asymptotic properties including the asymptotic relative efficiency are discussed for various score functions.

• Journal of the Korean Statistical Society
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• v.18 no.2
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• pp.85-96
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• 1989

This paper is concerned with the strong consistency of the least squares estimators for the nonlinear regression models. A simple and practical sufficient condition for the strong consistency of the least squares estimators is given. It is also discussed that the extension of the strong consistency to a wide class of regression functions can be established by imposing some condition on the input values. Some examples are given to illustrate the application of main result.