scholarly journals Mayfield Logistic Regression: A Practical Approach for Analysis of Nest Survival

The Auk ◽  
2004 ◽  
Vol 121 (3) ◽  
pp. 707-716
Author(s):  
Kirsten R. Hazler
Keyword(s):  
Logistic Regression ◽  
Survival Analysis ◽  
Survival Data ◽  
Habitat Structure ◽  
Nest Survival ◽  
Seasonal Effects ◽  
Analysis Framework ◽  
Explanatory Variables ◽  
Mayfield Method ◽  
Experimental Treatments

Abstract Mayfield logistic regression is a method for analyzing nest-survival data that extends the traditional Mayfield estimator by incorporating explanatory variables (e.g. habitat structure, seasonal effects, or experimental treatments) in a logistic-regression analysis framework. Although Aebischer (1999) previously showed that logistic regression can be used to fit Mayfield models, few ornithologists have put that finding into practice. My purpose here is to reintroduce this underused method of nest-survival analysis, to compare its performance to that of a dedicated survival-analysis program (MARK), and to provide a practical guide for its use. Like the traditional Mayfield method, Mayfield logistic regression accounts for the num ber of “exposure days” for each nest and allows for uncertain fates (censoring), thus avoiding the bias introduced by typical applications of logistic regression. Mayfield logistic regression should be widely applicable when nests are found at various stages in the nesting cycle and multiple explanatory variables influencing nest survival are of interest.

Discrete choice and survival models in employee turnover analysis

2018 ◽  
Vol 40 (2) ◽  
pp. 381-395 ◽  
Author(s):  
Rafa Madariaga ◽  
Ramon Oller ◽  
Joan Carles Martori
Keyword(s):  
Logistic Regression ◽  
Survival Analysis ◽  
Regression Model ◽  
Discrete Choice ◽  
Survival Data ◽  
Employee Turnover ◽  
Logistic Regression Model ◽  
Data Set ◽  
Content Type ◽  
Set Up

Purpose The purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover. Design/methodology/approach The authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated. Findings When no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study). Originality/value This paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

scholarly journals A Unified Approach to Analyzing Nest Success

The Auk ◽  
2004 ◽  
Vol 121 (2) ◽  
pp. 526-540 ◽  
Author(s):  
Terry L. Shaffer
Keyword(s):  
Logistic Regression ◽  
Linear Models ◽  
Nest Success ◽  
Nest Survival ◽  
Exposure Model ◽  
Survival Models ◽  
Suitable Alternative ◽  
Explanatory Variables ◽  
Exposure Method ◽  
Time Specific

Abstract Logistic regression has become increasingly popular for modeling nest success in terms of nest-specific explanatory variables. However, logistic regression models for nest fate are inappropriate when applied to data from nests found at various ages, for the same reason that the apparent estimator of nest success is biased (i.e. older clutches are more likely to be successful than younger clutches). A generalized linear model is presented and illustrated that gives ornithologists access to a flexible, suitable alternative to logistic regression that is appropriate when exposure periods vary, as they usually do. Unlike the Mayfield method (1961, 1975) and the logistic regression method of Aebischer (1999), the logistic-exposure model requires no assumptions about when nest losses occur. Nest survival models involving continuous and categorical explanatory variables, multiway classifications, and time-specific (e.g. nest age) and random effects are easily implemented with the logistic-exposure model. Application of the model to a sample of Yellow-breasted Chat (Icteria virens) nests shows that logistic-exposure estimates for individual levels of categorical explanatory variables agree closely with estimates obtained with Johnson (1979) constant-survival estimator. Use of the logistic-exposure method to model time-specific effects of nest age and date on survival of Blue-winged Teal (Anas discors) and Mallard (A. platyrhynchos) nests gives results comparable to those reported by Klett and Johnson (1982). However, the logistic-exposure approach is less subjective and much easier to implement than Klett and Johnson's method. In addition, logistic-exposure survival rate estimates are constrained to the (0,1) interval, whereas Klett and Johnson estimates are not. When applied to a sample of Mountain Plover (Charadrius montanus) nests, the logistic-exposure method gives results either identical to, or similar to, those obtained with the nest survival model in program MARK (White and Burnham 1999). I illustrate how the combination of generalized linear models and information-theoretic techniques for model selection, along with commonly available statistical software, provides ornithologists with a powerful, easily used approach to analyzing nest success.

Diabetes Mellitus in Patients With Cystic Fibrosis: Effect on Survival

PEDIATRICS ◽  
1990 ◽  
Vol 86 (3) ◽  
pp. 374-377
Author(s):  
J. Reisman ◽  
M. Corey ◽  
G. Canny ◽  
H. Levison
Keyword(s):  
Diabetes Mellitus ◽  
Cystic Fibrosis ◽  
Survival Analysis ◽  
Nutritional Status ◽  
Pulmonary Function ◽  
Survival Data ◽  
Diabetes Diagnosis ◽  
Patients With Diabetes ◽  
Insulin Dependent ◽  
Sick Children

Patient data obtained from the cystic fibrosis clinic of the Hospital for Sick Children (Toronto, Canada) over the period 1977 to 1988 were analyzed to compare the diabetic and nondiabetic cystic fibrosis patients. The pulmonary function, nutritional status, and survival data for 713 patients who attended the clinic over the 11-year period are reported. Insulin-dependent diabetes was found to exist in 37 (5.2%) of 713 patients. The patient age at time of diabetes diagnosis ranged from 2 to 34 years, with a mean ± SD of 20.0 ± 7.4 years. Patients who died in both the diabetic and nondiabetic groups had worse pulmonary and nutritional status than the surviving patients, but there were no significant differences between the diabetic and nondiabetic groups in those who died or in those who remained alive. Survival analysis showed a similar prognosis in the diabetic and nondiabetic groups. It is concluded that cystic fibrosis patients with diabetes are, for their age, not different from patients without diabetes with respect to pulmonary function, nutritional status, and survival.

Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

2019 ◽  
Vol 28 (1) ◽  
pp. 35 ◽  
Author(s):  
Pablo Pozzobon de Bem ◽  
Osmar Abílio de Carvalho Júnior ◽  
Eraldo Aparecido Trondoli Matricardi ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Federal District ◽  
Burned Area ◽  
Ann Model ◽  
Wildfire Management ◽  
Explanatory Variables ◽  
Burned Areas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.

scholarly journals Illuminance affects epidemiological parameters of banana Yellow Sigatoka in Brazil

2019 ◽  
Author(s):  
Djalma M. Santana-Filho ◽  
Milene C. da Silva ◽  
Jorge T. de Souza ◽  
Zilton J. M. Cordeiro ◽  
Hermínio S. Rocha ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Survival Analysis ◽  
Cropping Systems ◽  
Leaf Age ◽  
Cropping System ◽  
Black Sigatoka ◽  
Light Interference ◽  
Leaf Spots ◽  
Leaf Necrosis ◽  
Epidemiological Parameters

ABSTRACTThe Sigatoka leaf spots are among the most important banana diseases. Although less damaging than black sigatoka, yellow sigatoka (Pseudocercospora musae) still prevails in some regions. This study aimed at testing the hypothesis of light interference in monocyclic parameters of yellow sigatoka epidemics. Grande Naine plantlets kept under contrasting shading conditions had their leaves 1 and 2 inoculated. Evaluations were performed for 60 days. For each inoculated leaf, the time until symptom onset (incubation), presence of infectious lesions (latency), and disease severity (extensive leaf necrosis) according to Stover’s scale modify per Gauhl (1994), called here only Stover’s scale, were registered. Logistic regression was used to assess the relative occurrence risk and survival analysis was used to check the effects of variables on relevant epidemiological parameters. The risks of sporulation and of reaching high severities were lower for plants kept under shading regardless of the acclimation conditions and no effect of leaf age was detected. The logistic regression showed symptoms appearing in both conditions (p=0,85), but have significance difference in occurrence of latent lesions (p=0,013) and necrosis (p<0,0001). The necrosis risk in non-shaded environment arrived 66%. The survival analysis showed significance difference in the time to appear the symptom evaluated in all tested variables (p<0,0001) in function of the cropping system. Lower illuminance negatively affected the incubation, latency and infectious periods, and severity. A shaded system could be tested to produce organic bananas in areas of high risk of occurrence of Yellow sigatoka disease.Significance and Impact of the StudyYellow Sigatoka (Pseudocercospora musae) is a banana disease that can cause severe damage if left uncontrolled. Its control is based mostly on fungicides.Our results show that shading downregulates the epidemiological parameters of that disease such as incubation, latent and infectious periods, and symptom’s severity. These results can be the basis for testing alternative cropping systems and producing organic bananas.

scholarly journals A comprehensive analysis on cigarettes smoking and its determinants in Bangladesh using logistic regression model

2020 ◽  
Vol 8 (5) ◽  
pp. 1733
Author(s):  
Afrin Sadia Rumana ◽  
Asia Khatun ◽  
Sukanta Das
Keyword(s):  
Logistic Regression ◽  
Causes Of Death ◽  
Significant Contribution ◽  
Religious Affiliation ◽  
Economic Status ◽  
Bivariate Analysis ◽  
Adult Males ◽  
Online Questionnaire ◽  
Explanatory Variables ◽  
Household Economic Status

Background: In Bangladesh, smoking is one of the leading preventable causes of death. Despite possessing knowledge about the consequences of smoking and the resultant non-communicable diseases, individuals have become considerably habituated to it. The study aims to identify the factors associated with smoking cigarettes and as well as to examine the existing situation of this issue among adult males in Bangladesh.Methods: Total 480 adult males were surveyed from Bangladesh through personal interview and online questionnaire, (from June 2018 to June 2019). To measure the effect of the explanatory variables on cigarettes smoking, authors perform χ2 test of independence as bivariate analysis. After performing bivariate analysis, a logistic regression analysis has been performed to assess the effect of the explanatory variables.Results: Findings of the study revealed that educational level, household economic status, media exposure, division have significant contribution for smoking cigarettes among the adult male in Bangladesh. A comparison of religious affiliation showed smoking cigarettes to be higher among non-muslim counterparts. Respondents living in rural area are found to have smoking cigarettes comparing with urban area.Conclusions: From the study it can be concluded that education and socio-economic status of male make a significant contribution in cigarettes smoking.

scholarly journals Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

2018 ◽  
Vol 2 (334) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Data ◽  
Logistic Regression Model ◽  
Binary Classification ◽  
Classification Problem ◽  
Classification Rule ◽  
Unknown Parameters ◽  
Explanatory Variables

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

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