Examining the Reliability of Logistic Regression Estimation Software

Research Methods/ Statistical Methods,

Sample Size and Robustness of Inferences from Logistic Regression in the Presence of Nonlinearity and Multicollinearity

The logistic regression models has been widely used in the social and natural sciences and results from studies using this model can have significant impact. Thus, confidence in the reliability of inferences drawn from these models is essential. The robustness of such inferences is dependent on sample size. The purpose of this study is to examine the impact of sample size on the mean estimated bias and efficiency of parameter estimation and inference for the logistic regression model. A number of simulations are conducted examining the impact of sample size, nonlinear predictors, and multicollinearity on substantive inferences (e.g. odds ratios, marginal effects) and goodness of fit (e.g. pseudo-R2, predictability) of logistic regression models. Findings suggest that sample size can affect parameter estimates and inferences in the presence of multicollinearity and nonlinear predictor functions, but marginal effects estimates are relatively robust to sample size.Logistic Regression Model, Multicollinearity, Nonlinearity, Robustness, Small Sample Bias, Research Methods/ Statistical Methods,

Examining Inferences from Neural Network Estimators of Binary Choice Processes: Marginal Effects, and Willingness-to-Pay

To satisfy the utility maximization hypothesis in binary choice modeling, logit and probit models must make a priori assumptions regarding the underlying functional form of a representative utility function. Such theoretical restrictions may leave the postulated estimable model statistically misspecified. This may lead to significant bias in substantive inferences, such as willingness-to-pay (or accept) measures, in environmental, natural resource and applied economic studies. Feed-forward back-propagation artificial neural networks (FFBANN) provide a potentially powerful semi-nonparametric method to avoid potential misspecifications and provide more valid inference. This paper shows that a single-hidden layer FFBANN can be interpreted as a logistic regression with a flexible index function and can be subsequently used for statistical inference purposes, such as estimation of marginal effects and willingness-to-pay measures. To the authors’ knowledge, the derivation and estimation of marginal effects and other substantive measures using neural networks are not available in the economics literature and is thus a novel contribution. An empirical application is conducted using FFBANNs to demonstrate estimation of marginal effects and willingness-to-pay in a contingent valuation and stated choice experimental framework. We find that FFBANNs can replicate results from binary choice models commonly used in the applied economics literature and can improve on substantive inferences derived from these models

Field-Level Land-Use Adaptation to Local Weather Trends

The intersection of agriculture and climate has been well researched for at least the last couple of decades. Largely, the motivation for previous research has been the potential impact on food security for the world's (growing) population. Many studies have predicted unfavorable yield scenarios for some geographic regions. As a result, another common research theme is farmer adaptation to a changing climate. Typically, these studies are concerned with what farmers could or should do to adapt to adverse outcomes. However, research examining whether farmers respond to weather patterns has largely been ignored. Answering this question can help provide more accurate food security analyses: if farmers do respond to changing patterns through cropping decisions, for instance, the global food supply outcome will be different than a world in which they do not respond. This article aims to provide insights into what and how farmers' cropping decisions respond to weather patterns. The study region is a set of eleven Kansas counties. The article provides an important step toward more credible estimates of global food supplies under changing climates and the methods themselves translate to other areas. Results suggest that land-use responses to changing weather patterns will vary across time and space

On the examination of the reliability of statistical software for estimating regression models with discrete dependent variables

The numerical reliability of statistical software packages was examined for logistic regression models, including SAS 9.4, MATLAB R2015b, R 3.3.1., Stata/IC 14, and LIMDEP 10. Thirty unique benchmark datasets were created by simulating alternative conditional binary choice processes examining rare events, near-multicollinearity, quasi-separation and nonlinear transformation of variables. Certified benchmark estimates for parameters and standard errors of associated datasets were obtained following standards set-out by the National Institute of Standards and Technology. The logarithm of relative error was used as a measure of accuracy for numerical reliability. The paper finds that choice of software package and procedure for estimating logistic regressions will impact accuracy and use of default settings in these packages may significantly reduce reliability of results in different situations

Examining the relationship between vertical coordination strategies and technical efficiency: Evidence from the Brazilian ethanol industry

The sugarcane industry in Brazil, one of the world's leading producers of ethanol and sugar, is undergoing significant changes driven by geographic expansion and technological innovations. These changes are forcing sugarcane producers and processors, to re-evaluate their vertical coordination and growth strategies. This paper presents an empirical analysis of the relationship between the vertical coordination strategies at the production-processing interface of the Brazilian ethanol supply chain and the technical efficiency of the mills. It utilizes data envelopment analysis and a Tobit censored model in combination with unique data on 204 mills that account for around half of Brazil's sugar and ethanol production. Results indicate that vertical integration and the location of the mill have a statistically significant impact on efficiency. The findings show that the technical efficiency is not the main driver of vertical integration implying that such decisions are primarily motivated by strategic considerations. The mills are likely to forgo gains in technical efficiency in exchange for improving their strategic position through vertical integration. These findings shed light on the underlying motivation for the observed level of vertical integration that accompanies the expansion of the Brazilian sugarcane industry. [EconLit citations: L22, Q12, Q16]

US agricultural university students\u27 mental well-being and resilience during the first wave of COVID-19: Discordant expectations and experiences across genders

The coronavirus disease 2019 (COVID-19) pandemic\u27s first wave led to declining mental health and life satisfaction outcomes for college students, especially women. While women in undergraduate agricultural programs outperformed men academically prior to and during the pandemic, the achievement may have come at personal cost, especially for those women with fewer personal and environmental resiliency resources. Our research objective was to expand on personal, social, and environmental factors linked with lower mental health and life satisfaction scores for students in agriculture during the pandemic. We measured the influence of such factors across gender-based mental health and life satisfaction outcomes. Our data were collected from 2030 students using an on-line survey across six land-grant university college of agriculture in agriculturally as many distinct regions of the United States. We estimated OLS and Ordered Probit models of their mental health and life satisfaction self-assessments. Our findings reveal students\u27 mental health and life satisfaction were reduced due to a paucity of personal (e.g., less future orientation or graduate school aspirations, food and housing insecurity, and personal health risks) and environmental (e.g., lower quality on-line learning experiences, isolation, family health risk, discrimination experiences) resiliency resources. Our results suggest women were more likely than men to be adversely affected by reduced resiliency resources. These findings suggest university emergency response policies need to address students\u27 needs for housing and food security, on-line course development and delivery, tele health and mental health resources, broad social inclusion and diversity to decrease risk of female attrition and support all students in agricultural degree programs

Factors affecting farmers’ willingness to grow alternative biofuel feedstocks across Kansas

Energy conservation has emerged as one of the biggest challenges of the world in the XXI century, and not different from many countries, the US has created plans and policies to stimulate renewable energy alternative. Among the important alternatives for energy conservation is the use of biomass energy. Despite these stimuli production predictions are not confident that production would achieve the planned target for the U.S. Consequently, the predictions raise questions about farmer's willingness to grow bioenergy crops or produce alternative cellulosic feedstocks. In other words, farmers and landholders may not be willing to grow bioenergy crops. With this concerns in mind, the study advances previous research about bioenergy production by evaluating farmer's and landholder's willingness to produce different varieties of biofuel feedstocks. To achieve our goals, we used a mail survey of Kansas farmers conducted from January to April of 2011. The survey contained questions related to how farmers make their land-use decisions covering a wide array of topics. Through this survey, we evaluate the effect of farm characteristics, farm management practices, farmer perceptions (such as risk aversion), physical variables (such as soil, weather, and the availability of water for irrigation) on farmers' willingness to produce value-added feedstocks (e.g., corn stover), dedicated annual bioenergy crops (e.g., energy sorghum), and dedicated perennial bioenergy crops (e.g., switchgrass) for biofuel production in Kansas, though the use of logistic regressions and marginal effects

Study of avidity of antigen-specific antibody as a means of understanding development of long-term immunological memory after Vibrio cholerae O1 infection

The avidity of antibodies to specific antigens and the relationship of avidity to memory B cell responses to these antigens have not been studied in patients with cholera or those receiving oral cholera vaccines. We measured the avidity of antibodies to cholera toxin B subunit (CTB) and Vibrio cholerae O1 lipopolysaccharide (LPS) in Bangladeshi adult cholera patients (n = 30), as well as vaccinees (n = 30) after administration of two doses of a killed oral cholera vaccine. We assessed antibody and memory B cell responses at the acute stage in patients or prior to vaccination in vaccinees and then in follow-up over a year. Both patients and vaccinees mounted CTB-specific IgG and IgA antibodies of high avidity. Patients showed longer persistence of these antibodies than vaccinees, with persistence lasting in patients up to day 270 to 360. The avidity of LPS-specific IgG and IgA antibodies in patients remained elevated up to 180 days of follow-up. Vaccinees mounted highly avid LPS-specific antibodies at day 17 (3 days after the second dose of vaccine), but the avidity waned rapidly to baseline by 30 days. We examined the correlation between antigen-specific memory B cell responses and avidity indices for both antigens. We found that numbers of CTB- and LPS-specific memory B cells significantly correlated with the avidity indices of the corresponding antibodies (P < 0.05; Spearman's ρ = 0.28 to 0.45). These findings suggest that antibody avidity after infection and immunization is a good correlate of the development and maintenance of memory B cell responses to Vibrio cholerae O1 antigens

Modeling lymphocyte homing and encounters in lymph nodes

International audienceBackgroundThe efficiency of lymph nodes depends on tissue structure and organization, which allow the coordination of lymphocyte traffic. Despite their essential role, our understanding of lymph node specific mechanisms is still incomplete and currently a topic of intense research.ResultsIn this paper, we present a hybrid discrete/continuous model of the lymph node, accounting for differences in cell velocity and chemotactic response, influenced by the spatial compartmentalization of the lymph node and the regulation of cells migration, encounter, and antigen presentation during the inflammation process.ConclusionOur model reproduces the correct timing of an immune response, including the observed time delay between duplication of T helper cells and duplication of B cells in response to antigen exposure. Furthermore, we investigate the consequences of the absence of dendritic cells at different times during infection, and the dependence of system dynamics on the regulation of lymphocyte exit from lymph nodes. In both cases, the model predicts the emergence of an impaired immune response, i.e., the response is significantly reduced in magnitude. Dendritic cell removal is also shown to delay the response time with respect to normal conditions