• Communications for Statistical Applications and Methods
• /
• v.26 no.2
• /
• pp.191-203
• /
• 2019

The problem of examining how well an assumed distribution fits the data of a sample is of significant and must be examined prior to any inferential process. The observed failure time data of items are often not wholly available in reliability and life-testing studies. Lowering the expense and period associated with tests is important in statistical tests with censored data. Goodness-of-fit tests for perfect data can no longer be used when the observed failure time data are progressive Type II censored (PC) data. Therefore, we propose goodness-of-fit test statistics and a graphical method based on generalized Lorenz curve for PC data from a location-scale distribution. The power of the proposed tests is then assessed through Monte Carlo simulations. Finally, we analyzed two real data set for illustrative purposes.

• The Journal of Korean Academy of Prosthodontics
• /
• v.36 no.4
• /
• pp.587-597
• /
• 1998

In this study, the Periotest value was measured with Periotest to evaluate precision fit of the 2-unit and 3-unit implant-supported prosthesis by modifying the size and location of ill-fitted conditions. The 2-unit prosthesis was fabricated with the right implant fitted incorrectly and the 3-unit prosthesis with the right and center implant fitted incorrectly. To evaluate the effects of the ill-fitted sizes, 4 groups were divided.:The control group being the accurately fabricated sample group fitted properly. Group 1 was constructed with $40{\mu}m$ ill-fitted conditions, group 2 with $70{\mu}m$ and group 3 with $100{\mu}m$ ill-fitted conditions. The Periotest value was measured at each implant site after tightening 10Ncm. The result was follows : 1. The PTV on the ill-fitted area in the 2-unit implant-supported prosthesis increased as the ill-fitted conditions increased. There was a statistically significant difference among groups(p<0.05). In the same ill-fitted sample, the PTV depending on the measured location demonstrated a statistically significant difference (p<0.05) 2. The PTV on the ill-fitted area of the 3-unit implant-supported with an ill-fitted condition in the right implant increased as the ill-fitted conditions increased. There was a significant difference among groups (p<0.05). In the same ill-fitted sample, the PTV depending on the measured location demonstrated a statistically significant difference (p<0.05). 3. In the 3-unit implant-supported prosthesis with ill fitting conditions in the center implant, the PTV on the ill-fitted area demonstrated a statistically significant difference between the control group, group 1 and group 2 (p<0.05). In the same ill-fitted sample, the PTV depending on the measured location demonstrated significant difference between the gap side and the adjacent side with over $70{\mu}m$ ill-fitted conditions (p<0.05). The results suggest that Periotest is a valuable objective method for evaluating the precision fit of an implant superstructure.

• Journal of the Korean Data and Information Science Society
• /
• v.13 no.1
• /
• pp.113-119
• /
• 2002

A powerful and easily computed goodness-of-fit test for Pareto distribution which does not depend on the unknown location and scale parameters is proposed based on the transformed sample Lorenz curve. We compare the power of the proposed test statistic with the other goodness-of-fit tests for Pareto distribution against various alternatives through Monte Carlo methods.

• Journal of the Korean Data and Information Science Society
• /
• v.13 no.2
• /
• pp.251-260
• /
• 2002

The aim of this paper is to consider of detecting the location, the jump size and the number of change-points in regression functions by using the local linear fit which is one of nonparametric regression techniques. It is obtained the asymptotic properties of the change points and the jump sizes. and the correspondin grates of convergence for change-point estimators.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.12 no.2
• /
• pp.25-29
• /
• 2016

Owing to the change of economic trends, the importance of the tourism industry is growing more and more. In particular, the number of foreign tourists continues to increase and the type of tourists is being changed into FIT (Foreign Independent Traveler). Therefore it is an important issue to provide the effective information to foreign tourists. To solve these problems, a variety of IT technology is being used in the tourism information systems. Especially the location based tour information service is being emerged. This kinds of tourism information service is a type of LBS (Location Based Services). But if the security of the location based tourism information service is not guaranteed, it can lead to many dangers. In this paper, the trends of location based tourism information service are surveyed. Also the security threats and countermeasures for the location based tourism information service are analyzed. This paper suggests secret considerations for the location based tourism information service.

• Communications for Statistical Applications and Methods
• /
• v.10 no.3
• /
• pp.859-871
• /
• 2003

In this paper we introduce some adaptive M-estimators using selector statistics to estimate the slope of regression model under the symmetric and continuous underlying error distributions. This selector statistics is based on the residuals after the preliminary fit L$_1$ (least absolute estimator) and the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying symmetric distributions in the location model. If we use L$_1$ as a preliminary fit to get residuals, we find the asymptotic distribution of sample quantiles of residual are slightly different from that of sample quantiles in the location model. If we use the functions of sample quantiles of residuals as selector statistics, we find the suitable quantile points of residual based on maximizing the asymptotic distance index to discriminate distributions under consideration. In Monte Carlo study, this adaptive M-estimation method using selector statistics works pretty good in wide range of underlying error distributions.

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.05a
• /
• pp.133-137
• /
• 2002

A powerful and easily computed goodness-of-fit test for Pareto distribution which does not depend on the unknown location and scale parameters is proposed based on the transformed sample Lorenz curve. We compare the power of the proposed test statistic with the other goodness-of-fit tests for Pareto distribution against various alternatives through Monte Carlo methods.

• Communications for Statistical Applications and Methods
• /
• v.10 no.1
• /
• pp.31-38
• /
• 2003

A simple method is proposed to detect the number of change points and test the location and size of multiple change points with jump discontinuities in an otherwise smooth regression model. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Our proposed methodology is explained and applied to real data and simulated data.

• The Korean Journal of Applied Statistics
• /
• v.26 no.4
• /
• pp.611-622
• /
• 2013

Mudholkar and Tian (2002) proposed an entropy-based test of fit for the inverse Gaussian distribution; however, the test can be applied to only the composite hypothesis of the inverse Gaussian distribution with an unknown location parameter. In this paper, we propose an entropy-based goodness-of-fit test for an inverse Gaussian distribution that can be applied to the composite hypothesis of the inverse Gaussian distribution as well as the simple hypothesis of the inverse Gaussian distribution with a specified location parameter. The proposed test is based on the probability integration transformation. The critical values of the test statistic estimated by simulations are presented in a tabular form. A simulation study is performed to compare the proposed test under some selected alternatives with Mudholkar and Tian (2002)'s test in terms of power. The results show that the proposed test has better power than the previous entropy-based test.

• Journal of the Korean Statistical Society
• /
• v.25 no.3
• /
• pp.347-358
• /
• 1996

A new test for goodness-of-fit is presented. It is a modification of a test of LaRiccia (1991). These tests are applicable to continuous lo-cation/scale models. The new test statistic is based on a few selected order statistics taken from the sample, while the LaRiccia test is based directly on the full sample. Each test embeds the hypothesized model in a larger linear model and proceeds to test the goodness-of-fit hy-pothesis by testing the coefficients of this linear model appropriately. The general theory is presented. The tests are compared via computer simulation to a related test of Ali and Umbach (1989) for distributions that could be used as lifetime models. An important aspect of all these tests is that only standard $X_2$ tables are used. Selection of the spacings of the order statistics is discussed.