• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.625-634
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• 2005

A procedure is proposed to identify multiple outliers in the logistic regression. It detects the leverage points by means of hierarchical clustering of the robust distances based on the minimum covariance determinant estimator, and then it employs a V-mask type criterion on the scatter plot of robust residuals against robust distances to classify the observations into vertical outliers, bad leverage points, good leverage points, and regular points. Effectiveness of the proposed procedure is evaluated on the basis of the classic and artificial data sets, and it is shown that the procedure deals very well with the masking and swamping effects.

• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.102-111
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• 1997

In this paper I discuss a method of constructing the confidence region for the logistic response surface model. The construction involves a, pp.ication of a general fitting procedure because the log odds is linear in its parameters. Estimation of parameters of the logistic response surface model can be accomplished by maximum likelihood, although this requires iterative computational method. Using the asymptotic results, asymptotic covariance of the estimators can be obtained. This can be used in the construction of confidence regions for the parameters and for the logistic response surface model.

• Journal of Preventive Medicine and Public Health
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• v.15 no.1
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• pp.179-186
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• 1982

The methods controlling the confounding factors were discussed using the data of secondary infertility with induced abortion. Mantel-Haenszel method and logistic model were applied in the analysis to find out which factors were confounding and/or effect modification variables. In the logistic analysis, the main effect of induced abortion, spontaneous abortion, age and interaction effect between induced abortion and spontaneous abortion were chosen as independent variables being regressed into logistic functions. Spontaneons abortion was interpreted as a potential confounder and at the same time potential effect modifier and age was interpreted as potential confounder. Spontaneous abortion was shown to be more important influencing factor than age to the secondary infertility. In the course of logistic analysis, the problem of parameter estimation and hypothesis testing, assessing the fitness of a model, and selection of the best model were briefly explained. For the program of logistic model, FUNCAT Procedure of SAS package was chosen.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.457-469
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• 2007

Many procedures are available to identify a single outlier or an isolated influential point in linear regression and logistic regression. But the detection of influential points or multiple outliers is more difficult, owing to masking and swamping problems. The multiple outlier detection methods for logistic regression have not been studied from the points of direct procedure yet. In this paper we consider the direct methods for logistic regression by extending the $Pe\tilde{n}a$ and Yohai (1995) influence matrix algorithm. We define the influence matrix in logistic regression by using Cook's distance in logistic regression, and test multiple outliers by using the mean shift model. To show accuracy of the proposed multiple outlier detection algorithm, we simulate artificial data including multiple outliers with masking and swamping.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.129-140
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• 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 Data and Information Science Society
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• v.13 no.2
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• pp.129-138
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• 2002

This paper deals with logistic regression models for analysing separation rates from majors. The model building procedure shows how to incoporate the effects of some factors causing from three-way nested sampling scheme and discusses what type of characteristics as independent variables directly affecting the rates should be considered.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.783-791
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• 2008

Estimation procedure of the finite population proportion and distribution function is considered. Based on a logistic regression model, an approximately model- optimal estimator is defined and conditions for the estimator to be design-consistent are given. Simulation study shows that the model-optimal design-consistent estimator defined under a logistic regression model performs well in estimating the finite population distribution function.

• Management & Information Systems Review
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• v.7
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• pp.427-450
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• 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.

• Clinics in Shoulder and Elbow
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• v.26 no.3
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• pp.252-259
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• 2023

Background: The purpose of this study was to identify demographics and risk factors associated with unplanned 30-day readmission and reoperation following open procedures for shoulder instability and examine recent trends in open shoulder instability procedures. Methods: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried using current procedural terminology (CPT) codes 23455, 23460, and 23462 to find patients who underwent shoulder instability surgery from 2015 to 2019. Independent sample Student t-tests and chi-square tests were used in univariate analyses to identify demographic, lifestyle, and perioperative variables related to 30-day readmission following repair for shoulder instability. Multivariate logistic regression modeling was subsequently performed. Results: In total, 1,942 cases of open surgical procedures for shoulder instability were identified. Within our study sample, 1.27% of patients were readmitted within 30 days of surgery, and 0.85% required reoperation. Multivariate logistic regression modeling confirmed that the following patient variables were associated with a statistically significant increase in the odds of readmission: open anterior bone block/Latarjet-Bristow procedure, being a current smoker, and a long hospital stay (all P<0.05). Multivariate logistic regression modeling confirmed statistically significant increased odds of reoperation with an open anterior bone block or Latarjet-Bristow procedure (P<0.05). Conclusions: Unplanned 30-day readmission and reoperation after open shoulder instability surgery is infrequent. Patients who are current smokers, have an open anterior bone block or Latarjet-Bristow procedure, or a longer than average hospital stay have higher odds of readmission than others. Patients who undergo an open anterior bone block or Latarjet-Bristow procedure have higher odds of reoperation than those who undergo an open soft-tissue procedure. Level of evidence: III.