scholarly journals Willingness to Pay of Fishermen Insurance Using Logistic Regression with Parameter Estimated by Maximum Likelihood Estimation Based on Newton Raphson Iteration

2021 ◽  
Vol 17 (1) ◽  
pp. 15
Author(s):  
Yulianus - Brahmantyo ◽  
Riaman Riaman ◽  
F Sukono
Keyword(s):  
Genetic Algorithm ◽  
Logistic Regression ◽  
Willingness To Pay ◽  
Logistic Regression Model ◽  
Likelihood Estimation ◽  
Fishing Community ◽  
Community Membership ◽  
Newton Raphson ◽  
Main Factors ◽  
Raphson Iteration

The high risk of losing fishermen's life while at sea is inversely proportional to their low welfare. Fishermen are also unable to meet their daily needs when they are not going to sea. Fishermen welfare insurance can be a solution for them to meet their daily needs. Willingness to Pay (WTP) of fishermen to participate in fishermen welfare insurance can be analyzed using Logistic Regression with Newton Raphson and Genetic Algorithm approximations. Some of the main factors that can support their WTP to participate in fishermen welfare insurance, are fishermen education, membership in the fishing community, membership in fisherman business cards, and knowledge about the existence of fishermen insurance. From these four factors, Logistic Regression Model is generated which is expected to help the increase of fishermen’s WTP on fishermen insurance in Indonesia.

scholarly journals Willingness to Pay for Conservation of the Rong Por’s Community Forest in Phayao Province

2021 ◽  
Vol 2 (2) ◽  
pp. 70-76
Author(s):  
Anupong Wongchai ◽  
Lin Yi-Chia
Keyword(s):  
Logistic Regression ◽  
Willingness To Pay ◽  
National Park ◽  
Likelihood Estimation ◽  
Local Area ◽  
Primary Data ◽  
Community Forest ◽  
Factors Affecting ◽  
Park Area ◽  
Forest Lands

Rong Por community forest was declared to be included in the Doi Luang National Park since 1981, according to the Parliament, Act of 1961. It is the cause of conflict of interest related to government projects and possessory right of land ownership because the houses were in the Doi Luang National Park area. Moreover, the local people were accused of the invasion of forest lands from government officials cause people locals to express themselves as precedent residents the announcement of a national park clearly expressed was not invading.  Therefore, the purposes of this research aimed to study on willingness to pay for conservation of the Rong Por’s community forest and to analyze the factors affecting the willingness to pay for conservation of Rong Por’s community forest located in Dongjen Sub-District, Phukamyao District, Phayao Province, Thailand. The primary data were collected by a questionnaire, a total of 400 sample sizes. The logistic regression with Maximum Likelihood Estimation (MLE) was theoretically employed to analyze what factors affecting the values of willingness to pay. The empirical results showed that the respondents are unwilling to pay for conservation because they were confirmed that they were not intruders. Moreover, the analysis from Logistic Regression depicted that the factors affecting the willingness to pay for forest conservation are more benefits to this research and can be used as the guidelines for the policy-maker in the local area to conserve the Rong Por’s community forest.

scholarly journals Forecasting Of Content Ca 125 Endometriosis Using Logistic Regression Model

2020 ◽  
Vol 5 (1) ◽  
pp. 47
Author(s):  
Sardjana Atmadja ◽  
Gulam Gumilar
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Measurement Model ◽  
Ca 125 ◽  
P Value ◽  
Primary Dysmenorrhoea ◽  
Lemeshow Test ◽  
Main Factors ◽  
Blood Loss Volume

Objective : This study is to prove that there is a significant relationship between the absence of students participating in activities at school / on campus and the symptoms of primary dysmenorrhoea experienced during menstruation. Endometriosis is characterized as pain under the abdomen during menstruation. In addition, this study is also to obtain a profile of students and factors that influence primary dysmenorrhoea. A logistic regression model has been used to assess the main factors of dysmenorrhoea among these students.Methods : The study was conducted at the RSK Permata Hati Malang. A total of 123 students were randomly selected in this study. The factors observed were menarche, menstruation, menstruation period and blood loss volume and CA 125 level. From the logistic regression model, it was found that there were three factors that influence the occurrence of dysmenorrhoea among students, namely menarche, menstruation period and menstrual blood volume.Results: The Hosmer and Lemeshow test showed that the measurement model of CA 125 levels in endometriosis was appropriate (Chi squar test value was 2.847 with p-value = 0.416). Instead of Press. (3) and Eq. (4), it was found that the contributors to dismenortea were menstrual length, menstrual discharge and the beginning of menarche. By looking at the odds ratio it is found that the risk of students experiencing dysmenorrhoea is (i) 2.5 times higher for those with longer menstrual periods (ii) 3.7 times higher for those who have menstrual expenditure which is a little and (iii) three times higher for those who have mined it for more than 13 years.Conclusion: Significant CA 125 levels were obtained for students and students suffering from dysmenorrhoea. The study also found that the risk of getting dysmenorrhoea increased if students and students had menstrual periods longer than 35 days, menstrual expenditure levels were small and menarche was more than 13 years old.International Journal of Human and Health Sciences Vol. 05 No. 01 January’21 Page: 47-49

scholarly journals Variable selection in Logistic regression model with genetic algorithm

2018 ◽  
Vol 6 (3) ◽  
pp. 45-45 ◽  
Author(s):  
Zhongheng Zhang ◽  
Victor Trevino ◽  
Sayed Shahabuddin Hoseini ◽  
Smaranda Belciug ◽  
Arumugam Manivanna Boopathi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Logistic Regression ◽  
Variable Selection ◽  
Regression Model ◽  
Logistic Regression Model

scholarly journals Some New Robust Estimators for Circular Logistic Regression Model with Applications on Meteorological and Ecological Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shokrya S. Alshqaq ◽  
Abdullah A. Ahmadini ◽  
Ali H. Abuzaid
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Likelihood Estimation ◽  
Simulation Studies ◽  
Robust Estimators ◽  
Finite Sample ◽  
Ecological Data ◽  
Logistic Regression Models

Maximum likelihood estimation ( MLE ) is often used to estimate the parameters of the circular logistic regression model due to its efficiency under a parametric model. However, evidence has shown that the classical MLE extremely affects the parameter estimation in the presence of outliers. This article discusses the effect of outliers on circular logistic regression and extends four robust estimators, namely, Mallows, Schweppe, Bianco and Yohai estimator BY , and weighted BY estimators, to the circular logistic regression model. These estimators have been successfully used in linear logistic regression models for the same purpose. The four proposed robust estimators are compared with the classical MLE through simulation studies. They demonstrate satisfactory finite sample performance in the presence of misclassified errors and leverage points. Meteorological and ecological datasets are analyzed for illustration.

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