scholarly journals Pemodelan Semiparametrik Geographical Weighted Logistic Regression pada Data Kemiskinan di Provinsi Sulawesi Selatan Tahun 2017

2021 ◽  
pp. 105-114
Author(s):  
Fitriatusakiah Fitriatusakiah ◽  
Andi Kresna Jaya ◽  
La Podje Talangko
Keyword(s):  
Logistic Regression ◽  
Likelihood Estimation ◽  
Semiparametric Model ◽  
Parameter Estimator ◽  
Location Factor ◽  
Factors Affecting ◽  
South Sulawesi ◽  
Weighted Logistic Regression ◽  
Poverty Status ◽  
Maximum Method

The level of poverty in a Regency/city in South Sulawesi in 2017 is different. The grouping of poverty status can be done based on the value of the HeadCount Index (HCI) of South Sulawesi. Factors affecting poverty will differ for each area being observed. The statistical modeling method developed for data analysis by taking into account the location factor is semiparametric Geographical Weighted Logistic Regression (GWLR). The GWLR semiparametric Model consists of parameters that are affected by the location and not affected by the location. The parameter estimator of the GWLR semiparametric model used in this research was obtained using the maximum method likelihood estimation. The result of a semiparametric model of GWLR each district/city in South Sulawesi in 2017 has the value Estimator parameter for global parameters is the same value for each location, namely, a3 = 0.1724, a4 = 0.0204, and a6 = 0.0261 whereas the parameter estimator for local parameters has different values so that GWLR semiparametric model of each district/city.

scholarly journals Model Regresi Logistik Terboboti Georafis pada Status Kemiskinan Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2016

2019 ◽  
Vol 1 (3) ◽  
pp. 87
Author(s):  
Sadriana Rustan ◽  
Muhammad Arif Tiro ◽  
Muhammad Nadjib Bustan
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Geographical Location ◽  
Predictor Variable ◽  
Likelihood Estimation ◽  
Local Form ◽  
South Sulawesi ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

Abstrak. Analisis regresi logistik digunakan untuk menentukan hubungan antara peubah respon bersifat kategori dengan satu atau lebih peubah penjelas dengan asumsi bahwa respon tidak dipengaruhi oleh lokasi geografis (data spasial). Salah satu metode analisis spasial adalah Model Regresi Logistik Terboboti Geografis (RLTG). Model RLTG adalah bentuk regresi logistik lokal di mana lokasi geografis diperhatikan dan diasumsikan memiliki distribusi Bernoulli. Pendugaan parameter model RLTG menggunakan metode Maximum Likelihood Estimation (MLE) dengan memberikan bobot yang berbeda pada lokasi yang berbeda. Data dalam penelitian ini diperoleh dari publikasi Badan Pusat Statistik, yaitu data dan Informasi Kemiskinan di Provinsi Sulawesi Selatan. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi status kemiskinan di Provinsi Sulawesi Selatan dengan menggunakan model regresi logistik terboboti geografis dengan fungsi pembobot Kernel bisquare. Hasil penelitian menunjukkan bahwa peubah penjelas yang mempengaruhi status kemiskinan di Provinsi Sulawesi Selatan adalah persentase penduduk tidak bekerja dan persentase rumah tangga pengguna jamban bersama.Abstract. Logistic regression a analysis is used to determine the relationship between categorical response variables with one or more predictor variable assuming that the response is not influenced by geographical location (spatial data). One method of spatial analysis is Geographically Weighted Logistic Regression (GWLR). The GWLR model is a local form of logistic regression where the geographical location is considered and assumed to have a Bernoulli distribution. Estimating parameters of the RLTG model uses the Maximum Likelihood Estimation (MLE) method by giving different weights to different locations. The data were obtained from BPS publications, namely Data and Information on Poverty in South Sulawesi Province. This study aims to determine the factors that influence poverty status in South Sulawesi Province using a geographically weighted logistic regression model with kernel bisquare weighting function. The results showed that the explanatory variables that influence the status of poverty in the province of South Sulawesi were the percentage of the population not working and the percentage of common household toilet users.Keywords: logistic regression, kernel bisquare, GWLR and poverty.

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 ESTIMASI PARAMETER MODEL GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Fika Dian Lestari, Dadan Kusnandar, Naomi Nessyana Debataraja
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

Metode Geographically Weighted Logistic Regression (GWLR) merupakan pengembangan metode regresi logistik dengan mempertimbangkan faktor letak geografis. Faktor letak geografis ini digunakan sebagai pembobot dan menunjukkan sifat lokal pada model GWLR. Metode ini digunakan ketika data memiliki pengaruh heterokedastisitas spasial. Penaksiran parameter model GWLR menggunakan metode Maximum Likelihood Estimation (MLE). Sedangkan pengujian yang dilakukan untuk mengetahui variabel yang signifikan adalah dengan metode uji Wald. Tujuan penelitian ini adalah untuk mengetahui proses estimasi parameter dalam model GWLR dengan pembobot yang digunakan merupakan fungsi kernel. Hasil penelitian menunjukkan model GWLR untuk jumlah variabel lebih dari dua adalah . Kata Kunci: Regresi logistik, pembobot, faktor letak geografis.

Analysis on application level based on ordinal logistic regression and best of advanced manufacturing technologies (AMT) selection based on fuzzy-TOPSIS integration approach

2021 ◽  
pp. 1-11
Author(s):  
Guilian Wang ◽  
Liyan Zhang ◽  
Jing Guo
Keyword(s):  
Logistic Regression ◽  
Organizational Factors ◽  
Fuzzy Topsis ◽  
Ordinal Logistic Regression ◽  
Industrial Robots ◽  
Policy Implications ◽  
Market Pricing ◽  
Factors Affecting ◽  
Pricing Power ◽  
The Core Competence

This paper try to fully reveal the key factors affecting the the level of AMT application in micro- and small enterprises (MSEs) from its organizational factors by ordinal logistic regression. The results show that MSEs have a relatively high level of AMT application as a whole due to the maturity and cost reduction of basic technologies such as artificial intelligence, digital manufacturing and industrial robots. In this paper we propose manufacturing world analysis at Application using Logistic Regression and best AMT selection using Fuzzy-TOPSIS Integration approach.Considering the influence mechanism of each factor, the important factors that affect the application level of AMT are the enterprise’s market pricing power, the main production types, technical, market and management capabilities, organization development incentives and the interaction with external stakeholders. Based on the results above, the following policy implications are proposed: further expanding the customized production in MSEs to gradually improve the market pricing power, expanding the core competence of enterprises, enhancing the employee autonomy, and strengthening the interaction with industry organizations.

scholarly journals Factors Affecting Salt Reduction Measure Adoption among Chinese Residents

2021 ◽  
Vol 18 (2) ◽  
pp. 445
Author(s):  
Zeying Huang ◽  
Di Zeng
Keyword(s):  
Logistic Regression ◽  
Food Intake ◽  
Nutrition Education ◽  
Online Survey ◽  
Adaptive Lasso ◽  
Stepwise Logistic Regression ◽  
Salt Reduction ◽  
Salt Restriction ◽  
Salt Diet ◽  
Factors Affecting

China has the highest mortality rate caused by diseases and conditions associated with its high-salt diet. Since 2016, China has initiated a national salt reduction campaign that aims at promoting the usage of salt information on food labels and salt-restriction spoons and reducing condiment and pickled food intake. However, factors affecting individuals’ decisions to adopt these salt reduction measures remain largely unknown. By comparing the performances of logistic regression, stepwise logistic regression, lasso logistic regression and adaptive lasso logistic regression, this study aims to fill this gap by analyzing the adoption behaviour of 1610 individuals from a nationally representative online survey. It was found that the practices were far from adopted and only 26.40%, 22.98%, 33.54% and 37.20% reported the adoption of labelled salt information, salt-restriction spoons, reduced condiment use in home cooking and reduced pickled food intake, respectively. Knowledge on salt, the perceived benefits of salt reduction, participation in nutrition education and training programs on sodium reduction were positively associated with using salt information labels. Adoption of the other measures was largely explained by people’s awareness of hypertension risks and taste preferences. It is therefore recommended that policy interventions should enhance Chinese individuals’ knowledge of salt, raise the awareness of the benefits associated with a low-salt diet and the risks associated with consuming excessive salt and reshape their taste choices.

Identifying the Factors Affecting the Probability of Accident Severity in Community Road - Focusing on Seoul City

2014 ◽  
Vol 641-642 ◽  
pp. 860-865
Author(s):  
You Jin Lim ◽  
Hak Ryong Moon ◽  
Won Pyoung Kang
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Regression Technique ◽  
Road Accidents ◽  
Factors Affecting ◽  
Accident Severity

Since a variety of factors are associated with crash occurrence, the analysis of causes of crash is a hard task for traffic researchers and engineers. This study was attempted to identify factors affecting severity of the community road accidents. In particular, our analyses were focused on the community road accidents. A binary logistic regression technique was adopted for the analyses. The results showed that pedestrians of 65 years or older, cloudy, fence (sidewalk/driveway barrier), drivers of 24 years or younger, left/right turning, female pedestrian, non-business vehicle were dominant factors for the severity.

Implementation of Machine Learning Algorithms for Prediction of Fluidelastic Instability in Tube Arrays

2021 ◽  
Vol 143 (2) ◽  
Author(s):  
Joaquin E. Moran ◽  
Yasser Selima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Two Phase ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Number Of Factors ◽  
Tube Arrays ◽  
Fluidelastic Instability

Abstract Fluidelastic instability (FEI) in tube arrays has been studied extensively experimentally and theoretically for the last 50 years, due to its potential to cause significant damage in short periods. Incidents similar to those observed at San Onofre Nuclear Generating Station indicate that the problem is not yet fully understood, probably due to the large number of factors affecting the phenomenon. In this study, a new approach for the analysis and interpretation of FEI data using machine learning (ML) algorithms is explored. FEI data for both single and two-phase flows have been collected from the literature and utilized for training a machine learning algorithm in order to either provide estimates of the reduced velocity (single and two-phase) or indicate if the bundle is stable or unstable under certain conditions (two-phase). The analysis included the use of logistic regression as a classification algorithm for two-phase flow problems to determine if specific conditions produce a stable or unstable response. The results of this study provide some insight into the capability and potential of logistic regression models to analyze FEI if appropriate quantities of experimental data are available.

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