• Communications for Statistical Applications and Methods
• /
• v.4 no.1
• /
• pp.177-183
• /
• 1997

A data-adaptive order selection procedure is proposed for local polynomial nonparametric regression. For each given polynomial order, bias and variance are estimated and the adaptive polynomial order that has the smallest estimated mean squared error is selected locally at each location point. To estimate mean squared error, empirical bias estimate of Ruppert (1995) and local polynomial variance estimate of Ruppert, Wand, Wand, Holst and Hossjer (1995) are used. Since the proposed method does not require fitting polynomial model of order higher than the model order, it is simpler than the order selection method proposed by Fan and Gijbels (1995b).

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.2
• /
• pp.535-545
• /
• 2015

In a various range of applications including hydrology, the type-I extreme value distribution has been extensively used as a probabilistic model for analyzing extreme events. In this paper, we introduce methods for estimating the scale parameter of the type-I extreme value distribution. A simulation study is performed to compare the estimators in terms of mean-squared error and bias, and the obtained results are provided.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.1
• /
• pp.213-220
• /
• 2006

Since Beran (1977) developed the minimum Hellinger distance estimation, this method has been a popular topic in the field of robust estimation. In the process of defining a distance, a kernel density estimator has been widely used as a density estimator. In this article, however, we show that a combination of a kernel density estimator and an empirical density could result a smaller bias of the minimum Hellinger distance estimator than using just a kernel density estimator for a location parameter.

• Journal of the Korean Data and Information Science Society
• /
• v.6 no.1
• /
• pp.73-83
• /
• 1995

We construct the approximate bootstrap confidence intervals for reliability (R) when the distributions of strength and stress are both normal. Also we propose percentile, bias correct (BC), bias correct acceleration (BCa), and percentile-t intervals for R. We compare with the accuracy of the proposed bootstrap confidence intervals and classical confidence interval based on asymptotic normal distribution through Monte Carlo simulation. Results indicate that the confidence intervals by bootstrap method work better than classical confidence interval. In particular, confidence intervals by BC and BCa method work well for small sample and/or large value of true reliability.

• The Korean Journal of Applied Statistics
• /
• v.29 no.4
• /
• pp.643-656
• /
• 2016

High dimensional data are frequently encountered in various fields where the number of variables is greater than the number of samples. It is usually necessary to select variables to estimate regression coefficients and avoid overfitting in high dimensional data. A penalized regression model simultaneously obtains variable selection and estimation of coefficients which makes them frequently used for high dimensional data. However, the penalized regression model also needs to select the optimal model by choosing a tuning parameter based on the model selection criterion. This study deals with the bias effect of LASSO regression for model selection criteria. We numerically describes the bias effect to the model selection criteria and apply the proposed correction to the identification of biomarkers for lung cancer based on gene expression data.

• Journal of the Korean Geotechnical Society
• /
• v.16 no.1
• /
• pp.145-155
• /
• 2000

Orientation data of discontinuities are of paramount importance for rock slope stability studies because they control the possibility of unstable conditions or excessive deformation. Most orientation data are collected by using linear sampling techniques, such as borehole fracture mapping and the detailed scanline method (outcrop mapping). However, these data, acquired by the above linear sampling techniques, are subjected to bias, owing to the orientation of the sampling line. Even though a weighting factor is applied to orientation data in order to reduce this bias, the bias will not be significantly reduced when certain sampling orientations are involved. That is, if the linear sampling orientation nearly parallels the discontinuity orientation, most discontinuities orientation data which are parallel to sampling line will be excluded from the survey result. This phenomenon can cause serious misinterpretation of discontinuity orientation data because critical information is omitted. In the case study, orientation data collected by using the borehole fracture mapping method (vertical scanline) were compared to those based on orientation data from the detailed scanline method (horizontal scanline). Differences in results for the two procedures revealed a concern that a representative orientation of discontinuities was not accomplished. Equal-area, polar stereo nets were used to determine the distribution of dip angles and to compare the data distribution fur the borehole method versus those for the scanline method.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.8
• /
• pp.190-199
• /
• 2019

This quantitative study analyzed the influence of college students' preferred type of occupation on a confirmation bias and job anxiety during the process of making a career decision. The questionnaires were distributed to university students in Seoul and the metropolitan area for 500 weeks from July 10 2017 to August 8, 2017. Among them, 482 valid samples of data were analyzed by data coding and data cleaning usin SPSS 18.0 statistics and the AMOS 18.0 program. The main results of this study are that the type of business preference for an affirmative bias has a positive (+) direct influence (${\beta}=.374$) and the type of freedom has a positive direct influence (${\beta}=.326$) and a negative direct influence (${\beta}=-.274$). In the case of job anxiety, the influence of job type is more increased. The confirmation bias shows that the business type and freestyle type find cause in effort or achievement motive, while rect type is recognized as social environment and structural problem. In conclusion, there is a difference in the degree of confirmation bias and job insecurity. This study shows that college students' preferred occupation types can help them to understand the bias and anxiety that they have in preparing for the job and help to reduce job anxiety, and these findings are expected to be useful for career guidance.

• Journal of the Korean Data and Information Science Society
• /
• v.5 no.2
• /
• pp.19-27
• /
• 1994

It is known that the maximum likelihood method does not provide explicit estimator for the scale parameter of the Weibull distribution based on Type-II censored samples. In this paper we provide an approximate maximum likelihood estimator (AMLE) of the scale parameter of the Weibull distribution with Type-II censoring. We obtain the asymptotic variance and simulate the values of the bias and the variance of this estimator based on 3000 Monte Carlo runs for n = 10(10)30 and r,s = 0(1)4. We also simulate the absolute biases of the MLE and the proposed AMLE for complete samples. It is found that the absolute bias of the AMLE is smaller than the absolute bias of the MLE.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.4
• /
• pp.1029-1039
• /
• 2006

There are different kinds of methods to estimate the odds ratio for unemployment statistics in small areas, namely, the composite estimator, the Woolf estimator and the Mantel-Haenszel estimator. We can compare the reliability of these estimators according to the bias and MSE. The estimation procedures considered by this study have been applied to estimate the bias and MSE of the odds ratio between the male and female unemployment rate in some small areas. The Woolf estimator or the Mantel-Haenszel estimator is more stable than the composite estimator, but all these three estimators are similar to each other from the aspect of efficiency.

• Journal of Astronomy and Space Sciences
• /
• v.27 no.3
• /
• pp.213-220
• /
• 2010

As an observation instrument of the longest record of tropospheric water vapor, radiosonde data provide upper-air pressure (geopotential height), temperature, humidity and wind. However, the data have some well-known elements related to inaccuracy. In this article, radiosonde precipitable water vapor (PWV) at Sokcho observatory was compared with global positioning system (GPS) PWV during each summertime of year 2007 and 2008 and the biases were calculated. As a result, the mean bias showed negative values regardless of the rainfall occurrence. In addition, on the basis of GPS PWV, the maximum root mean square error (RMSE) was 5.67 mm over the radiosonde PWV.