Empirical distributions of parameter estimates in binary logistic regression using bootstrap

2014 ◽  
Vol 8 ◽  
pp. 721-726 ◽  
Author(s):  
Anwar Fitrianto ◽  
Ng Mei Cing
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Parameter Estimates ◽  
Empirical Distributions

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

scholarly journals Predictive Performance of Logistic Regression for Imbalanced Data with Categorical Covariate

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Hezlin Aryani Abd Rahman ◽  
Yap Bee Wah ◽  
Ong Seng Huat
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Imbalanced Data ◽  
Binary Logistic Regression ◽  
Predictive Performance ◽  
Parameter Estimate ◽  
Classification Performance ◽  
Parameter Estimates ◽  
Binary Dependent Variable ◽  
Simulation Results

Logistic regression is often used for the classification of a binary categorical dependent variable using various types of covariates (continuous or categorical). Imbalanced data will lead to biased parameter estimates and classification performance of the logistic regression model. Imbalanced data occurs when the number of cases in one category of the binary dependent variable is very much smaller than the other category. This simulation study investigates the effect of imbalanced data measured by imbalanced ratio on the parameter estimate of the binary logistic regression with a categorical covariate. Datasets were simulated with controlled different percentages of imbalance ratio (IR), from 1% to 50%, and for various sample sizes. The simulated datasets were then modeled using binary logistic regression. The bias in the estimates was measured using MSE (Mean Square Error). The simulation results provided evidence that the effect of imbalance ratio on the parameter estimate of the covariate decreased as sample size increased. The bias of the estimates depended on sample size whereby for sample size 100, 500, 1000 – 2000 and 2500 – 3500, the estimates were biased for IR below 30%, 10%, 5% and 2% respectively. Results also showed that parameter estimates were all biased at IR 1% for all sample size. An application using a real dataset supported the simulation results.

scholarly journals Analyzing data from memory tasks - comparison of ANOVA, logistic regression and mixed logit model

Psihologija ◽  
2018 ◽  
Vol 51 (4) ◽  
pp. 469-488
Author(s):  
Milica Popovic-Stijacic ◽  
Ljiljana Mihic ◽  
Dusica Filipovic-Djurdjevic
Keyword(s):  
Logistic Regression ◽  
Confidence Intervals ◽  
Logit Model ◽  
Linear Models ◽  
Mixed Logit ◽  
Binary Logistic Regression ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Mixed Logit Model ◽  
Mixed Logit Models

We compared three statistical analyses over binary outcomes. As applying ANOVA over proportions violates at least two classical assumptions of linear models, two alternatives are described: the binary logistic regression and the mixed logit model. Firstly, we compared the effects obtained by the three methods over the same data from a previous memory research. All three methods gave similar results: the effects of the tasks and the number of sensory modalities were observed, but not their interaction. Secondly, by using the bootstrap estimates of the parameters, the efficacy of each method was explored. As predicted, the bootstrap parameter estimates of the ANOVA had large bias and standard errors, and consequently wide confidence intervals. On the other hand, the bootstrap parameter estimates of the binary logistic regression and the mixed logit models were similar ? both had low bias and standard errors and narrow confidence intervals.

Environmental Justice: Segregation, Noise Pollution and Health Disparities near the Hartsfield-Jackson Airport Area in Atlanta

2021 ◽  
pp. 003464462110651
Author(s):  
Mona Ray
Keyword(s):  
Logistic Regression ◽  
Hearing Impairment ◽  
Residential Segregation ◽  
Sleep Disturbances ◽  
Noise Exposure ◽  
Binary Logistic Regression ◽  
Noise Pollution ◽  
Binary Logistic Regression Analysis ◽  
Parameter Estimates ◽  
Black And White

The inquiry in this paper has two parts: (1) an examination of potential disparities in exposure to airport noise pollution between Blacks (non-Hispanic) and Whites (non-Hispanic) around the Atlanta Hartsfield-Jackson airport (AHJA) area, and (2) a binary logistic regression analysis studying factors contributing to these disparities. The proposed model is that the difference in noise exposure measured by Net Exposure Difference score is a function of the degree of Black-White residential segregation; differences in poverty rates between Blacks and Whites; some socio-economic-demographic variables and four health indicators - noise annoyance (NA); sleep disturbance (SD); hearing impairment (HI); and cardiovascular disorder (CVD). A stratified random sampling method and telephonic survey using a 43-questions questionnaire among the adult households around the AHJA area produced 237 observations on Black and White households over a period of 2 years. Parameter estimates reveals disparities in exposure to aircraft noise exposure between the Black and White households within the 10-mile radius of the airport area indicating environmental injustice. The odds-ratios from the binary logistic regression suggests residential segregation, difference in poverty rates, race, education, as well as health conditions like hearing impairment and sleep disturbances have a statistically significant association with this disparity in noise exposure.

Predictive Performance of Logistic Regression for Imbalanced Data with Categorical Covariate

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Hezlin Aryani Abd Rahman ◽  
Yap Bee Wah ◽  
Ong Seng Huat
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Imbalanced Data ◽  
Binary Logistic Regression ◽  
Predictive Performance ◽  
Parameter Estimate ◽  
Classification Performance ◽  
Parameter Estimates ◽  
Binary Dependent Variable ◽  
Simulation Results

Logistic regression is often used for the classification of a binary categorical dependent variable using various types of covariates (continuous or categorical). Imbalanced data will lead to biased parameter estimates and classification performance of the logistic regression model. Imbalanced data occurs when the number of cases in one category of the binary dependent variable is very much smaller than the other category. This simulation study investigates the effect of imbalanced data measured by imbalanced ratio on the parameter estimate of the binary logistic regression with a categorical covariate. Datasets were simulated with controlled different percentages of imbalance ratio (IR), from 1% to 50%, and for various sample sizes. The simulated datasets were then modeled using binary logistic regression. The bias in the estimates was measured using Mean Square Error (MSE). The simulation results provided evidence that the effect of imbalance ratio on the parameter estimate of the covariate decreased as sample size increased. The bias of the estimated depends on sample size whereby for sample size 100, 500, 1000 - 2000 and 2500 - 3500, the estimated were biased for IR below 30%, 10%, 5% and 2% respectively. Results also showed that parameter estimates were all biased at IR 1% for all sample size. An application using a real dataset supported the simulation results.

Evaluating the implementation potential of a transcultural tool for Tamil migrants with gestational diabetes mellitus living in Switzerland

2017 ◽  
Vol 2 (2) ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Online Survey ◽  
Healthcare Professionals ◽  
Binary Logistic Regression ◽  
Private Practices ◽  
Populations At Risk ◽  
Management Procedures

Background: Gestational diabetes mellitus is a condition that affects many pregnancies and ethnicity appears to be a risk factor. Data indicate that approximately 18% of Tamil women are diagnosed with gestational diabetes mellitus. Today, approximately 50,000 of Tamils live in Switzerland. To date, there is no official tool available in Switzerland that considers the eating and physical activity habits of this migrant Tamil population living in Switzerland, while offering a quick overview of gestational diabetes mellitus and standard dietetics management procedures. The NutriGeD project led by Bern University of Applied Sciences in Switzerland aimed at closing this gap. The aim of this present study was to evaluate the implementation potential of the tools developed in the project NutriGeD for dietetic counseling before their wide scale launch in Swiss hospitals, clinics and private practices. Method: An online survey was developed and distributed to 50 recruited healthcare professionals working in the German speaking region of Switzerland from October – December 2016 (31% response rate). The transcultural tools were sent to participants together with the link to the online survey. The evaluation outcome was analysed using binary logistic regression and cross tabulation analysis with IBM SPSS version 24.0, 2016. Results: 94% (N=47) respondents believed that the transcultural tools had good potential for implementation in hospitals and private practices in Switzerland. A binary logistic regression analysis revealed that the age of participants had a good correlation (42.1%) on recommending the implementation potential of the transcultural tool. The participants with age group 34- 54 years old where the highest group to recommend the implementation potential of the transcultural tool and this was found to be statistically significant (p=0.05). 74% (34 out of 50) of the respondents clearly acknowledged the need for transcultural competence knowledge in healthcare practices. 80% (N =40) of the respondents agreed that the information presented in the counseling display folder was important and helpful while 60% (N= 30) agreed to the contents being clinically applicable. 90% (N=45) participants recommended the availability of the evaluated transcultural tools in healthcare settings in Switzerland. Conclusion: The availability in healthcare practice of the evaluated transcultural tools was greatly encouraged by the Swiss healthcare practitioners participating in the survey. While they confirmed the need for these transcultural tools, feed-backs for minor adjustments were given to finalize the tools before their official launch in practice. The developed materials will be made available for clinical visits, in both hospitals and private practices in Switzerland. The Migmapp© transcultural tool can serve as a good approach in assisting healthcare professionals in all fields, especially professionals who practice in areas associated with diet - related diseases or disorders associated with populations at risk.

Intimate Partner Violence and its Association With Contraceptive Use Among Women in Pakistan

2019 ◽  
Vol 34 (Spring 2019) ◽  
pp. 157-173
Author(s):  
Kashif Siddique ◽  
Rubeena Zakar ◽  
Ra’ana Malik ◽  
Naveeda Farhat ◽  
Farah Deeba
Keyword(s):  
Intimate Partner Violence ◽  
Logistic Regression ◽  
Partner Violence ◽  
Contraceptive Use ◽  
Binary Logistic Regression ◽  
Intimate Partner ◽  
Married Women ◽  
Reproductive Age ◽  
Outcome Variable ◽  
Odds Ratios

The aim of this study is to find the association between Intimate Partner Violence (IPV) and contraceptive use among married women in Pakistan. The analysis was conducted by using cross sectional secondary data from every married women of reproductive age 15-49 years who responded to domestic violence module (N = 3687) of the 2012-13 Pakistan Demographic and Health Survey. The association between contraceptive use (outcome variable) and IPV was measured by calculating unadjusted odds ratios and adjusted odds ratios with 95% confidence intervals using simple binary logistic regression and multivariable binary logistic regression. The result showed that out of 3687 women, majority of women 2126 (57.7%) were using contraceptive in their marital relationship. Among total, 1154 (31.3%) women experienced emotional IPV, 1045 (28.3%) women experienced physical IPV and 1402 (38%) women experienced both physical and emotional IPV together respectively. All types of IPV was significantly associated with contraceptive use and women who reported emotional IPV (AOR 1.44; 95% CI 1.23, 1.67), physical IPV (AOR 1.41; 95% CI 1.20, 1.65) and both emotional and physical IPV together (AOR 1.49; 95% CI 1.24, 1.72) were more likely to use contraceptives respectively. The study revealed that women who were living in violent relationship were more likely to use contraceptive in Pakistan. Still there is a need for women reproductive health services and government should take initiatives to promote family planning services, awareness and access to contraceptive method options for women to reduce unintended or mistimed pregnancies that occurred in violent relationships.

scholarly journals Characteristics and outcomes of children with dissociative (conversion) disorders in western China: a retrospective study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhixu Fang ◽  
Yuhang Li ◽  
Lingling Xie ◽  
Min Cheng ◽  
Jiannan Ma ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Environmental Variables ◽  
Binary Logistic Regression ◽  
Junior College ◽  
Conversion Disorder ◽  
Psychological Characteristics ◽  
Western China ◽  
Left Behind ◽  
Conversion Disorders ◽  
Close Relationship

Abstract Background Dissociative (conversion) disorder in children is a complex biopsychosocial disorder with high rates of medical and psychiatric comorbidities. We sought to identify the characteristics and outcomes of children with dissociative (conversion) disorders in western China. Methods We conducted a retrospective cohort study of 66 children admitted with dissociative (conversion) disorders from January 2017 to July 2019, and analyzed their clinical characteristics, socio-cultural environmental variables, and personality and psychiatric/psychological characteristics. Binary logistic regression was used to analyze the variables associated with clinical efficacy. Results Of these 66 patients, 38 (57.6%) were male and 28 (42.4%) were female, 46 (69.7%) had an antecedent stressor, 30 (45.5%) were left-behind adolescents, and 16 (24.2%) were from single-parent families. In addition, 30 patients (45.5%) were not close to their parents, 38 patients (59.4%) had an introverted personality, and 34 (53.1%) had unstable emotions. Thirteen families (19.7%) were uncooperative with the treatment. Patients who had cormorbid anxiety or depression exhibited significantly lower cognitive ability (P < 0.01). Logistic regression found that better treatment outcomes were positively associated with having a close relationship with parents, parental cooperation with treatment, and having a father with a lower level of education (i.e., less than junior college or higher). Conclusions The characteristics and outcomes of children with dissociative (conversion) disorders are related to socio-cultural environmental variables and psychiatric/psychological factors. Timely recognition and effective treatment of dissociative (conversion) disorders are important.

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