• Communications for Statistical Applications and Methods
• /
• 제10권2호
• /
• pp.277-289
• /
• 2003

We propose modified exact inferential methods in logistic regression model. Exact conditional distribution in logistic regression model is often highly discrete, and ordinary exact inference in logistic regression is conservative, because of the discreteness of the distribution. For the exact inference in logistic regression model we utilize the modified P-value. The modified P-value can not exceed the ordinary P-value, so the test of size $\alpha$ based on the modified P-value is less conservative. The modified exact confidence interval maintains at least a fixed confidence level but tends to be much narrower. The approach inverts results of a test with a modified P-value utilizing the test statistic and table probabilities in logistic regression model.

• Journal of the Korean Data and Information Science Society
• /
• 제25권1호
• /
• pp.195-209
• /
• 2014

In this paper, we derive the estimators of the location parameter and the scale parameter in a logistic distribution based on multiply type-II censored samples by the approximate maximum likelihood estimation method. We use four modified empirical distribution function (EDF) types test for the logistic distribution based on multiply type-II censored samples using proposed approximate maximum likelihood estimators. We also propose the modified normalized sample Lorenz curve plot for the logistic distribution based on multiply type-II censored samples. For each test, Monte Carlo techniques are used to generate the critical values. The powers of these tests are also investigated under several alternative distributions.

• Communications for Statistical Applications and Methods
• /
• 제27권1호
• /
• pp.129-140
• /
• 2020

In this paper, we introduce an efficient algorithm for the non-convex penalized multinomial logistic regression that can be uniformly applied to a class of non-convex penalties. The class includes most non-convex penalties such as the smoothly clipped absolute deviation, minimax concave and bridge penalties. The algorithm is developed based on the concave-convex procedure and modified local quadratic approximation algorithm. However, usual quadratic approximation may slow down computational speed since the dimension of the Hessian matrix depends on the number of categories of the output variable. For this issue, we use a uniform bound of the Hessian matrix in the quadratic approximation. The algorithm is available from the R package ncpen developed by the authors. Numerical studies via simulations and real data sets are provided for illustration.

• Journal of the Korean Statistical Society
• /
• 제13권1호
• /
• pp.48-56
• /
• 1984

This paper considers one-sample and two-sample test for the logistic function by means of Kolmororov-Smirnov type statistics. The standard tables used for the Kolmogorov-Smirnov test are valid only when the function is completely specified; but they are not valid if the parameters of function are estimated from the sample. This note presents modified tables for the Kolmogorov-Sminov type staistic. These tables can be used to test the hypothesis that a sample comes from a logistic function when shape parameter $(\alpha)$ and location parameter $(\beta)$ must be estimated from the sample by the method of maximum likelihood. Monte Carlo method is employed to calculate the criticla values of the test. The tables of the critical values are provided.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
• /
• pp.431-434
• /
• 2003

In this paper we introduce a modified objective function for fuzzy c-means clustering with logistic regression model in the presence of noise cluster. The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. In real application there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for the data.

• 경영과정보연구
• /
• 제7권
• /
• pp.427-450
• /
• 2001

This research suggests the estimation methodology of Logistics. This paper elucidates the main problems associated with estimation in the regression model. We review the methods for estimating the parameters in the model and introduce a modified procedure in which all models are fitted and combined to construct a combination of estimates. The resulting estimators are found to be as efficient as the maximum likelihood (ML) estimators in various cases. Our method requires more computations but has an advantage for large data sets. Also, it enables to detect particular features in the data structure. Examples of real data are used to illustrate the properties of the estimators. The backgrounds of estimation of logistic regression model is the increasing logistic environment importance today. In the first phase, we conduct an exploratory study to discuss 9 independent variables. In the second phase, we try to find the fittest logistic regression model. In the third phase, we calculate the logistic estimation using logistic regression model. The parameters of logistic regression model were estimated using ordinary least squares regression. The standard assumptions of OLS estimation were tested. The calculated value of the F-statistics for the logistic regression model is significant at the 5% level. The logistic regression model also explains a significant amount of variance in the dependent variable. The parameter estimates of the logistic regression model with t-statistics in parentheses are presented in Table. The object of this paper is to find the best logistic regression model to estimate the comparative accurate logistics.

• 한국임상수의학회지
• /
• 제40권3호
• /
• pp.238-241
• /
• 2023

This study determined the effect of ovarian status at the beginning of the modified Double-Ovsynch program on reproductive performance in dairy cows. In the study, 1,302 cows were treated with a modified Double-Ovsynch program at 56 days after calving. This program comprises administering gonadotropin-releasing hormones (GnRH), prostaglandin F_2α (PGF_2α) 10 days later, GnRH 3 days later, GnRH 7 days later, and GnRH 56 h later, followed by timed artificial insemination (TAI) 16 h later. At the beginning of the program, cows were categorized according to the size of the largest follicle and the presence of a corpus luteum (CL) in the ovaries as follows: 1) small follicle (<5 mm, SF group, n = 100), 2) medium follicle (8-20 mm, MF group, n = 538), and 3) large follicle (≥25 mm, LF group, n = 354) without a CL, or 4) the presence of a CL (CL group, n = 310). The pregnancies per AI after the first TAI were analyzed by logistic regression using the LOGISTIC procedure, and the logistic model included the fixed effects of the herd size, parity, body condition score (BCS) at the first TAI, TAI period, and ovarian status. A larger herd size, higher BCS at the first TAI, and TAI period with no heat stress increased (p < 0.05) the probability of pregnancy per AI after the first TAI. However, ovarian status at the beginning of the program did not affect (p > 0.05) the pregnancies per AI (ranges of 37.9% to 42.9%). These results show that the modified Double-Ovsynch program can be used effectively while maintaining good fertility regardless of the ovarian status in dairy herds.

• 대한토목학회논문집
• /
• 제32권6B호
• /
• pp.399-406
• /
• 2012

본 연구의 목적은 폐기물매립지에서의 온실가스 발생량 예측모델 및 모델에 적용된 변수들의 산정방법을 개발하는 것이다. 본 연구에서는 온실가스 발생예측 모델 중 1차 반응모델의 변수인 메탄잠재발생량과 메탄발생속도상수를 평가하기 위하여 수정 Gompertz 식과 Logistic 식을 미분한 2개의 식을 적용하였다. 변수들은 실제 폐기물매립지에서의 매립가스 발생량에 대한 실측값과 예측값과의 통계학적 비교를 통해 산정하였다. 매립가스 발생량에 대한 실측값과 수정 Gompertz 식 및 Logistic 식을 미분하여 나타낸 2개의 식을 이용한 매립가스 발생량 예측값에 대한 회귀분석결과 결정계수는 각각 0.92와 0.94로 나타나, 폐기물매립지에서의 매립가스 발생량에 대한 측정값이 있을 경우 회귀분석을 통해 변수를 산정할 수 있는 것으로 나타났다. 또한 실측값이 없는 폐기물매립지에서의 온실가스 발생량을 예측할 수 있도록 하기 위하여 수정 Gompertz 식과 Logistic 식을 미분한 2개의 식을 기초로 하여 예측모델을 개발하였으며, 이 모델들의 정확성을 평가하기 위하여 Qcs(실측값):Q(예측값)의 비에 대한 빈도분포를 평가한 결과 LandGEM 모델보다 높은 정확성을 나타내었다. 따라서 본 연구에서 개발한 모델들은 폐기물매립지에서의 온실가스 발생량 예측에 적합한 것으로 사료된다.

• Journal of the Korean Data and Information Science Society
• /
• 제21권2호
• /
• pp.317-325
• /
• 2010

In this paper, we develop four modified empirical distribution function (EDF) type tests using approximate maximum likelihood estimators for the half-logistic distribution based on multiply Type-II censored samples. We also propose modified normalize sample Lorenz curve polt and new test statistics. We compare the above test statistics in the sense of the power for various censored samples. We present an example to illustrate this method.

• Communications for Statistical Applications and Methods
• /
• 제29권1호
• /
• pp.1-25
• /
• 2022

A modified Bagdonavičius-Nikulin chi-square goodness-of-fit is defined and studied. The lymphoma data is analyzed using the modified goodness-of-fit test statistic. Different non-Bayesian estimation methods under complete samples schemes are considered, discussed and compared such as the maximum likelihood least square estimation method, the Cramer-von Mises estimation method, the weighted least square estimation method, the left tail-Anderson Darling estimation method and the right tail Anderson Darling estimation method. Numerical simulation studies are performed for comparing these estimation methods. The potentiality of the new model is illustrated using three real data sets and compared with many other well-known generalizations.