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Performance of some estimators for the multicollinear logistic regression model: theory, simulation, and applications

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Research in Statistics 2024年第1期2卷

作者： Md Ariful Hoque B. M. Golam Kibria Department of Mathematics and Statistics Florida International University Miami FL USA

.This article proposes some new estimators, namely Stein’s estimators for ridge regression and Kibria and Lukman estimator and compares their performance with some existing estimators, namely maximum likelihood estimator (MLE), ridge regression estimator, Liu estimator, almost unbiased ridge and Liu estimators, adjusted Liu estimator, James stein’s estimator, Kibria and Lukman estimator, Dorugade estimator, and modified ridge estimator for the logistic regression model to solve the multicollinearity problem. The bias, covariance matrix, and mean square error matrix for each of the estimators are provided. A Monte Carlo simulation has been conducted to compare the performance of different estimators. We consider the smaller mean squared error value as a performance criterion. From the simulation study, it is evident that all proposed estimators performed better than the MLE. Finally, a real-life data is analyzed to illustrate the findings of the article. Some promising estimators are recommended for the practitioners.

关键词： Almost unbiased estimator James-Stein estimator logistic regression model MSE multicollinearity ridge regression estimator

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An application of logistic regression model to the study of constitutional imbalances in human chromosomal fragile sites

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Research in Statistics 2024年第1期2卷

作者： Giovani L. Silva Inês J. Sequeira D. Prata Gomes José Rueff Aldina Brás a Centre of Statistics and its Applications (CEAUL) and Dep. Mathematics Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal b Center for Mathematics and Applications (NOVA Math) and Department of Mathematics NOVA FCT Lisbon Portugal c TOXOMICS NOVA Medical School/Faculdade de Ciências Médicas Universidade Nova de Lisboa Lisbon Portugal

Chromosomal fragile sites (CFSs) are loci or regions susceptible to spontaneous or induced occurrence of breaks and rearrangements. They are classified in two main categories, rare and common, depending on their frequency in the population. In order to identify which CFSs are influential or significant in the occurrence of deletions and duplications (chromosomal constitutional imbalances), we propose a logistic regression analysis for the CFS data set, since the underlying response variable is categorical, specifically binary (deletion or duplication). Some results are presented here as an informative preliminary contribution to understand the frailty of these CFS in increasing/decreasing of the deletion odds. This study has implications for our comprehension of human pathogenesis.

关键词： Chromosomal fragile sites deletions duplications logistic regression model

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Evaluation of a logistic regression model for predicting liver necroinflammation in hepatitis B e antigen-negative chronic hepatitis B patients with normal and minimally increased alanine aminotransferase levels

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JOURNAL OF VIRAL HEPATITIS 2019年第Sup1期26卷 42-49页

作者： Xie, Yao Yi, Wei Zhang, Lu Lu, Yao Hao, Hong-Xiao Gao, Yuan-Jiao Ran, Chong-Ping Lu, Hui-Hui Chen, Qi-Qi Shen, Ge Wu, Shu-Ling Chang, Ming Ping-Hu, Lei Liu, Rui-Yu Sun, Lei Wan, Gang Li, Ming-Hui Capital Med Univ Beijing Ditan Hosp Dept Hepatol Div 2 Jing Shun Dong Jie 8 Beijing 100015 Peoples R China Capital Med Univ Beijing Ditan Hosp Dept Gynecol & Obstet Beijing Peoples R China Capital Med Univ Beijing Ditan Hosp Dept Pathol Beijing Peoples R China Capital Med Univ Beijing Ditan Hosp Med Records & Stat Dept Beijing Peoples R China

Liver necroinflammation is the indicator for treating patients with chronic hepatitis B (CHB) infection. However, there is no suitable non-invasive index for diagnosing liver necroinflammation. This study aimed to create a non-invasive index to predict liver necroinflammation in patients who lack clear-cut clinical inflammation parameters. Patients who were hepatitis B e antigen (HBeAg)-negative and underwent liver histological diagnosis, had a normal or minimally increased alanine aminotransferase (ALT) level were enrolled. Liver necroinflammation was defined as histological active index >= 4. A logistic regression model (LRM) was established based on the parameters independently associated with liver necroinflammation. Of all 550 patients, 36.73% had necroinflammation. In patients with an abnormal ALT level, the rate of necroinflammation was 52.49%. The area under the curve (AUC) of the ALT level for predicting necroinflammation was 0.655 (95% confidence interval [CI], 0.609-0.702), and that of the HBV DNA level >= 2000 IU/mL combined with an abnormal ALT level was 0.618. By using the LRM, the AUC improved to 0.769 (95% CI, 0.723-0.815) with a Youden index of 0.519 and diagnostic accuracy of 75.3%. The cutoff value >= 0.7 in the LRM had a specificity of 97.4% and positive predictive value of 85.0% for predicting necroinflammation. By using the cutoff value <0.15 in the LRM, the presence of necroinflammation could be excluded with a negative predictive value of 90.8%. This study indicated that the LRM can be used to effectively diagnose liver necroinflammation in HBeAg-negative patients with CHB who have normal or minimally elevated ALT levels.

关键词： ALT level chronic hepatitis B HBeAg negative liver inflammation logistic regression model

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Food texture evaluation using logistic regression model and magnetic food texture sensor

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JOURNAL OF FOOD ENGINEERING 2018年第Apr.期222卷 20-28页

作者： Nakamoto, Hiroyuki Nishikubo, Daisuke Kobayashi, Futoshi Kobe Univ Grad Sch Syst Informat Nada Ku 1-1 Rokkodai Cho Kobe Hyogo 6578501 Japan

A food texture evaluation method using a magnetic food texture sensor is proposed for the visualization of food texture. The food texture sensor measures two time-series waves, one of force and one of vibration, during fracture of a food sample. Twenty profiles were extracted from the two waves. The evaluation method selected the profiles to use in the logistic model and determined the coefficients of the model based on the results of sensory tests. Laboratory experiments confirmed that the logistic model evaluated the food textures as radar charts. In addition, the model can potentially evaluate the food textures of unknown foods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Food texture Magnetic sensor logistic regression model Texture profile analysis

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Quantitative assessment of the effects of outside temperature on farrowing rate in gilts and sows by using a multivariate logistic regression model

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ANIMAL SCIENCE JOURNAL 2018年第8期89卷 1187-1193页

作者： Sasaki, Yosuke Fujie, Madoka Nakatake, Shingo Kawabata, Tadahiro Univ Miyazaki Org Promot Tenure Track Miyazaki 8892192 Japan Univ Miyazaki Ctr Anim Dis Control Miyazaki Japan Univ Miyazaki Fac Agr Dept Anim & Grassland Sci Miyazaki Japan Miyazaki Prefectural Econ Federat Agr Cooperat Miyazaki Japan Kagoshima Prefectural Econ Federat Agr Cooperat Sect Swine Kagoshima Japan

It is well known that pigs are sensitive to heat stress, but few studies have assessed the critical temperature that affects farrowing rate. Therefore, the objective of the present study was to assess the effects of outside temperature on farrowing rate by using a multivariate logistic regression model. Data were obtained from 25 commercial farms, including 26,128 service records for gilts and 120,655 service records for sows. Two variables, maximum temperature (MAX) and temperature humidity index (THI), were used as an indicator for climate conditions. In gilts, an interaction between climate conditions and service number was associated with farrowing rate (p<.05). In the first service, farrowing rate decreased as climate conditions increased, whereas no relationship was found in the second service or later. In sows, farrowing rate at first service decreased as MAX increased from 22 degrees C or THI increased from 66 (p<.05), but no apparent reduction under heat conditions was found in the second service or later. Additionally, effect of heat stress on farrowing rate in parities 1-2 was higher than those in parities 3-5 and 6 (p<.05). These results can be applied to field conditions as a standard for the critical temperature for farrowing rate.

关键词： farrowing rate heat stress logistic regression model sows temperature

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Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China

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GEOMATICS NATURAL HAZARDS & RISK 2018年第1期9卷 501-523页

作者： Xie, Peng Wen, Haijia Ma, Chaochao Baise, Laurie G. Zhang, Jialan Chongqing Univ Minist Educ Key Lab New Technol Construct Cities Mt Area Chongqing Peoples R China Chongqing Univ Sch Civil Engn Chongqing Peoples R China Tufts Univ Dept Civil & Environm Engn Medford MA 02155 USA

The main objective of this study is to evaluate the performances of different earthquake-induced landslides susceptibility mapping models at mountainous regions in China. At first, 160 earthquake-induced landslide points were identified from field investigations. Concurrently, based on the results of a literature review and the field investigation, 12 influencing factors were considered, and the corresponding thematic layers were generated using geographic information system (GIS) technology. Subsequently, 20 groups with a fixed number of cells were collected as a common training dataset for the two different models, based on a random selection from the entire database (including landslide cells and no-landslide cells). The neural network (NN) model and logistic regression (LR) model were developed with R software. Finally, earthquake-induced landslides susceptibility maps of Wenchuan county were produced, very low, low, medium, high and very high susceptibility zones cover. The validation results indicate that the landslide data from field investigations are in good agreement with the evaluation results, and the LR model has a slightly better prediction than the NN model in this case. In general, the NN model and LR models are satisfactory for susceptibility mapping of earthquake-induced landslides at mountainous regions.

关键词： Mountainous region earthquake-induced landslides susceptibility mapping logistic regression model neural network model

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Performance of the almost unbiased ridge-type principal component estimator in logistic regression model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2018年第10期47卷 2925-2937页

作者： Wu, Jibo Asar, Yasin Chongqing Univ Arts & Sci Sch Math & Finance Chongqing 402160 Peoples R China Necmettin Erbakan Univ Dept Math Comp Sci Konya Turkey

This article considers some different parameter estimation methods in logistic regression model. In order to overcome multicollinearity, the almost unbiased ridge-type principal component estimator is proposed. The scalar mean squared error of the proposed estimator is derived and its properties are investigated. Finally, a numerical example and a simulation study are presented to show the performance of the proposed estimator.

关键词： Almost unbiased ridge estimator Eigenvalues logistic regression model Principal component Scalar mean squared error

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Network Traffic Forecasting Based on logistic Iterative regression model 3

Network Traffic Forecasting Based on Logistic Iterative Regr...

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3rd IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Zhang Jianjun Xue Yuanbiao Feng Renhai Tianjin Opt Elect Grp Inst Radio Technol Tianjin Peoples R China Tianjin Univ Sch EIE Tianjin Peoples R China

ISBN: (数字)9781728188232

ISBN: (纸本)9781728188232

The size of the network traffic is of great significance to the design of the network architecture. This paper forecast network traffic based on logistic regression model, proposes an improved network traffic forecasting method. In this method, the logistic regression model parameters need to be estimated from historical data. For the three unknown parameters in the logistic regression model, first use the Neyman-Fisher factorization theorem to obtain the unbiased sufficient statistics of one of the parameters. Under the assumption that the general solution is known, use the least square method to solve the other two parameters. Then, under the premise of satisfying the constraints, the scope of the general solution is determined. Among all the parameters, the parameter with the smallest model error is selected to obtain the logistic regression prediction model. Experimental simulations prove that the method improves the accuracy of network traffic forecasting.

关键词： logistic regression model network traffic forecasting parameter estimation

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Predictors of tuberculosis: Application of a logistic regression model

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GENE REPORTS 2019年 17卷

作者： Ghazvini, Kiarash Yousefi, Masoud Firoozeh, Farzaneh Mansouri, Shamsoddin Mashhad Univ Med Sci Ghaem Hosp Antimicrobial Resistance Res Ctr Dept Microbiol Mashhad Razavi Khorasan Iran Alborz Univ Med Sci Sch Med Dept Microbiol Karaj Iran

Since the patients who suffer from tuberculosis when referring to the physician, they are without any specific symptoms of isolation and treatment and prevention of transmission of the disease to other people. This study aimed to determine the variables affecting tuberculosis using the logistic regression prediction model. This cross-sectional study enrolled 378 people (189 TB patients as a patients group and 189 healthy individuals as a control group) from Ghaem and Imam Reza hospitals, Mashhad (Iran) during March 2011 to December 2014. The variables affecting TB patients such as age, sex, and marital status, AIDS, smoking, history of asthma, organ transplantation, body mass index (BMI), vitamin D3 level, Diabetes, hemoglobin, and malignant diseases were compared in two groups. The sensitivity, specificity, ROC curve (Receiver operating characteristic) and positive and negative predictive values were used to evaluate the predictive power of logistic regression model. Data analyzed using SPSS software version 22 through logistic regression model and Chi-square test. And P-value < 0.05 were considered statistically significant. The sensitivity and specificity of this model in predict the tuberculosis were 78% and 68%, respectively. Also, the area under the curve (Roc) was 0.821. Variables;vitamin D3 (p = 0.01), hemoglobin (p = 0.01) and body mass index (BMI) (p = 0.01) significantly associated with tuberculosis. The results showed that the variables of vitamin D3, hemoglobin and body mass index (BMI) have a better prediction of TB in the logistic regression model.

关键词： logistic regression model Tuberculosis

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Improving predictive accuracy of logistic regression model using ranked set samples

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2017年第1期46卷 78-90页

作者： Santos, Kevin Carl P. Barrios, Erniel B. Univ Philippines Diliman Sch Stat Sch Stat BldgMagsaysay Ave Quezon City 1101 Philippines

logistic regression is often confronted with separation of likelihood problem, especially with unbalanced success-failure distribution. We propose to address this issue by drawing a ranked set sample (RSS). Simulation studies illustrated the advantages of logistic regression models fitted with RSS samples with small sample size regardless of the distribution of the binary response. As sample size increases, RSS eventually becomes comparable to SRS, but still has the advantage over SRS in mitigating the problem of separation of likelihood. Even in the presence of ranking errors, models from RSS samples yield higher predictive ability than its SRS counterpart.

关键词： logistic regression model Ranked set sampling Rare characteristics Separation of likelihood

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