• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.587-592
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• 2009

The most frequent use of the chi-square distribution is in the area of goodness-of-t of a distribution. The likelihood ratio test is a commonly used test statistic as the maximum likelihood estimate in statistical inferences. The recently revised versions of the likelihood ratio test statistics are used in estimating the parameter in the chi-square distribution. The estimates are compared with the commonly used method of moments and the maximum likelihood estimate.

• The Journal of Korean Physical Therapy
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• v.16 no.3
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• pp.97-111
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• 2004

This study, adopting the pretest-post test experimental study, is designed to find out how the functional electrical stimulation makes effect on the balance of a patient with spasticity of the ankle plantarflexor muscle caused by hemiplegic after stroke. The 46 subjects for this study were randomly sampled out of the patients who were hospitalized from September 1, 2003 to November 30, 2003 in H sanitarium in Yangpyung. The patients were with spasticity of the ankle plantarflexor muscle caused by hemiplegic after stroke and able to walk without supporting implements. The purpose of the study is firstly to analyze the change of ROM, FRTof a patient with spasticity of the ankle plantarflexor muscle when the functional electrical stimulation is applied and secondly to find out how the sex, age, height, weight, part of the diagnosis, duration of pain, experience of relapse and getting hurt from a fall of a patient make effect on the change. The experimental group for the study is divided into two to compare the differences of the effect. The exercising treatment only was performed for the conrtol group, and the functional electrical stimulation to the ankle dorsiflexor muscle as well as the exercising treatment was applied to the experimental group. The ROM test was performed to check the range of motion of the ankle with a double armed universal goniometer. The test was done 3 times to take an average. FRT were performed to check the balance. The statistical test was conducted using the SPSS 10.0/PC program by means of the following methods: χ2-test and t-test for testing homogeneity between the groups; paired t-test, independent sample t-test, F-test, and two-way ANOVA for analyzing the changes before and after the treatment. The levels of statistical significance of all the data were maintained at p<.05. According to the test, ROM has more decreased in experimental group than in control group when the functional electrical stimulation was applied only to the experimental group. However, the significant statistic difference was not shown (p=.059). FRT showed remarkable differences in the experimental group compared to the control group, showing the significant statistic difference (p=.000). On the one hand, the change of ROM, FRT related with the sex, age, height, weight, part of the diagnosis, and experience relapse was a meaningless minimum value. The change of ROM related to the duration of pain and the experience of falling down was also meaningless. However, FRT showed significant statistic difference (p<.05). According to the test above, the application of functional electrical stimulation to a patient with spasticity of the ankle dorsiflexor muscle caused by hemiplegic after stroke makes significant effect on the balance of a patient, but the result has nothing with the sex, age, height, weight, part of the diagnosis, duration of pain, experience of relapse and getting hurt from a fall of a patient. However, it is regarded to give contribution to the balance improvement of a patient. Therefore, this study expects to be a valuable clinical material for a patient with spasticity.

• Journal of Preventive Medicine and Public Health
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• v.38 no.2
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• pp.132-140
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• 2005

Objectives: To propose a risk-adjustment model from insurance claims data, and analyze the changes in cesarean section rates of healthcare organizations after adjusting for risk distribution. Methods: The study sample included delivery claims data from January to September, 2003. A risk-adjustment model was built using the 1st quarter data, and the 2nd and 3rd quarter data were used for a validation test. Patients' risk factors were adjusted using a logistic regression analysis. The c-statistic and Hosmer-Lemeshow test were used to evaluate the performance of the risk-adjustment model. Crude, predicted and risk-adjusted rates were calculated, and compared to analyze the effects of the adjustment. Results: Nine risk factors (malpresentation, eclampsia, malignancy, multiple pregnancies, problems in the placenta, previous Cesarean section, older mothers, bleeding and diabetes) were included in the final risk-adjustment model, and were found to have statistically significant effects on the mode of delivery. The c-statistic (0.78) and Hosmer-Lemeshow test ($x^2$=0.60, p=0.439) indicated a good model performance. After applying the 2nd and 3rd quarter data to the model, there were no differences in the c-statistic and Hosmer-Lemeshow $x^2$. Also, risk factor adjustment led to changes in the ranking of hospital Cesarean section rates, especially in tertiary and general hospitals. Conclusion: This study showed a model performance, using medical record abstracted data, was comparable to the results of previous studies. Insurance claims data can be used for identifying areas where risk factors should be adjusted. The changes in the ranking of hospital Cesarean section rates implied that crude rates can mislead people and therefore, the risk should be adjusted before the rates are released to the public. The proposed risk-adjustment model can be applied for the fair comparisons of the rates between hospitals.

• Journal of Korean Academy of Nursing
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• v.51 no.3
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• pp.280-293
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• 2021

Purpose: This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). Methods: This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. Results: The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. Conclusion: The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1397-1403
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• 2006

This paper considers the testing problem for scale changes in autoregressive processes with heavy-tailed innovations. For a test, we propose the CUSUM test statistic based on the trimmed residuals. We perform a simulation study for the mixture normal and Cauchy innovations.

• International Journal of Reliability and Applications
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• v.10 no.2
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• pp.81-88
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• 2009

A non-parametric procedure is presented for testing exponentially against used better than aged in convex ordering class (UBAC) of life distributions based on u-test. Convergence of the proposed statistic to the normal distribution is proved. Selected critical values are tabulated for sample sizes 5(5)40. The Pitman asymptotic relative efficiency of my proposed test to tests of other classes is studied. An example of 40 patients suffering from blood cancer disease demonstrates practical application of the proposed test.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.645-660
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• 2001

In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.789-798
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• 2000

The mean residual function is the expected remaining life of an item at age x. The problem of trend change in the mean residual life is great interest in the reliability and survival analysis. In this paper, we develop a family of test statistics for testing whether or not the mean residual life changes its trend. The asymptotic normality of the test statistics is established. Monte Carlo simulations are conducted to study the performance of our test statistics.

• Journal of Korean Society for Quality Management
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• v.19 no.2
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• pp.138-144
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• 1991

In this paper, we suggest the test statistic based on the total time on test (TTT-) transtorm for testing exponentiality against new better than used in convex ordering (NBUC) and its dual, new worse than used in convex ordering (NWUC). The validity of the test is examined by simulation for some alternative distributions when the sample size is n = 10 and n = 20.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.221-231
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• 2006

A chi-squared test of multivariate normality is suggested which is oriented for detecting deviations from elliptical symmetry. We derive the limiting distribution of the test statistic via a central limit theorem on empirical processes. A simulation study is conducted to study the accuracy of the limiting distribution in finite samples. Finally, we compare the power of our method with those of other popular tests of multivariate normality under a non-normal distribution.