Categorical Principal Component Logistic Regression: A Case Study for Housing Loan Approval

Kemalbay, Gülder; Korkmazoğlu, Özlem Berak

doi:10.1016/j.sbspro.2013.12.537

Cited by 24 publications

(13 citation statements)

References 5 publications

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“…X 1 = Age of household's head, X 2 = Gender of household's head, X 3 = Educational level, X 4 = Mortality level, X 5 = Household's feeding rate, X 6 = Extension visits on farm, X 7 = Total cost of production, X 8 = Farm income, X 9 = Receptivity to innovation, X 10 = Financial assistance, X 11 = Frequency of vaccination and X 12 = Health expenses). The logistic regression model is widely used to analyze data with dichotomous dependent variables (Kemalbay and Korkmazoğlu, 2014). It was considered a suitable model to use in this research because the dependent variable was dichotomous in nature.…”

Section: Logistic Regression Model (Lrm)mentioning

confidence: 99%

Empirical Analysis of Food Security Status of Agricultural Households in the Platinum Province of South Africa

Ijatuyi¹,

Omotayo²,

Nkonki-Mandleni³

2018

jard

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This paper gives a succinct report on an investigationinto the food security constraints of rural farminghouseholds in the North West Province of South Africa. Datapresented was gathered across the four districts, and was analyzedusing descriptive statistics and inferential statistics. Thedescriptive results showed that the mean age of rural farmerswas 55 years and the average household size was 7 members.Logistic regression results on factors influencing foodsecurity in the study showed that variables such as age of thehousehold’s head, household feeding rate, the total cost ofproduction, farm income and health expenditure had significantimpact on the respondents’ food security (at p < 0.05).As shown by Probit regression results, constraints such as theage of household head, food availability, veld fires, marketavailability, predator invasion, health expenditure and veterinarypractices significantly influenced the achievement offood security in the study area. This study concluded that ruralfarming households were witnessing different dimensions offood insecurity which affected different aspects of their socialand economic activities. It is therefore the responsibility of thegovernment to come up with a holistic approach to addressthe present discrepancy in the national and grass-roots foodsecurity status.

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Section: Logistic Regression Model (Lrm)mentioning

confidence: 99%

Empirical Analysis of Food Security Status of Agricultural Households in the Platinum Province of South Africa

Ijatuyi¹,

Omotayo²,

Nkonki-Mandleni³

2018

jard

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show abstract

“…A principal component analysis (PCA) method was used to carry out an exploratory factor analysis. Components were selected based on eigenvalue > 1 which is the coefficient of the principal components which shows the direction with the greatest variation (Kemalbay and Korkmazoğlu, 2014). For the smallholder realities variables, two variables were dropped in the first step of the factor analysis because their communality values were below 0.50.…”

Section: Factor Analysis Using the Principal Component Analysis (Pca) Methodsmentioning

confidence: 99%

Fostering bottom-up entrepreneurship in embryonic ecosystems: Insights from smallholders

Akiode¹

2021

AGROSH

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In embryonic entrepreneurial ecosystems, support policy frameworks seek to revitalize rural communities, alleviate poverty, transform key sectors of the economy and develop local businesses by increasing access to finance among others. However, policy frameworks rarely foster vertical connections across ecosystems particularly from the bottom-up. In some cases, entrepreneurial ecosystem elements do not necessarily address the peculiar needs of entrepreneurial diversity within the ecosystem. Drawing insights from smallholders, this paper seeks to examine the outlook for entrepreneurial opportunities among them and the influence of entrepreneurial ecosystem support on entrepreneurship opportunities outlook. The paper identifies the key drivers of bottom-up entrepreneurship opportunities. The study concludes that fostering bottom-up entrepreneurship among smallholders with a discovery entrepreneurship outlook in embryonic entrepreneurial ecosystems can contribute in helping those in poverty go beyond subsistence entrepreneurship. The paper recommends that entrepreneurial ecosystem elements should be mobilised to offer financial resources and institutional support beyond the current information support available to smallholders.

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“…tr is the trace function. e score matrix H is replaced by the matrix Q that has the categorical variables into numerical values [23]. en, the PCA analysis was conducted by software SAS 9.4 to replace the original corrected variables by uncorrelated principal components to regress the logistics model [21,33,34].…”

Section: Principle Component Analysis Selection Modelmentioning

confidence: 99%

“…is approach can be used to describe how various factors affect the violation behavior and to eliminate the multicollinearity in the observed data, further improving the measurement accuracy. PCA logistics models have been widely applied in biometrics [21], engineering application [22], economics [23], and management [24] fields to determine causality from collected data. Results of these studies indicated that the PCA model had high model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

Cai

Zhao

Liu

et al. 2020

Journal of Advanced Transportation

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Electric bikes play an important role in the urban transportation system in China. Yellow-light running behavior of riders is one of the most critical factors for e-bike riders involved in traffic crashes at intersection. The main purpose of this study is to explore how a variety of factors affect e-bike riders’ yellow-light running behaviors at intersection by a field observation conducted in Xi’an, China. Based on 396 e-bike riders who faced yellow-light samples, two analytical methods, the principle component analysis logistics model and a base logistics model, were employed to evaluate the impacts of contributing factors on e-bike riders’ yellow-light running behavior. The modeling results showed that seven variables significantly affect the e-bike riders’ yellow-light running behavior, which were the approaching speed of e-bike, the distance to stop line, riders’ age and gender attributes, type of e-bike, and the characteristics of intersection including the width of intersection and the existence of physical barriers. This study can provide valuable insights into understanding e-bike riders’ yellow-light running behavior and may also help decision makers propose countermeasures to reduce e-bike rider-related crashes at intersection.

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Categorical Principal Component Logistic Regression: A Case Study for Housing Loan Approval

Cited by 24 publications

References 5 publications

Empirical Analysis of Food Security Status of Agricultural Households in the Platinum Province of South Africa

Empirical Analysis of Food Security Status of Agricultural Households in the Platinum Province of South Africa

Fostering bottom-up entrepreneurship in embryonic ecosystems: Insights from smallholders

Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

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