Empirical Evidence on the Determinants of Rent-to-Own Use and Purchase Behavior

مطالعه حاضر با هدف بررسی نحوه اثرگذاری متغیرهای آب و هوایی شامل دما، بارش، سرعت باد و رطوبت بر سهم سطح زیرکشت انواع محصولات سالانه زراعی شامل غلات، حبوبات، سبزیجات، محصولات جالیزی، محصولات علوفه ای و محصولات صنعتی در ایران صورت گرفت. در این راستا با استفاده از اطلاعات زراعی و هواشناسی 336 شهرستان کشور در دوره زمانی 92-1391 اقدام به برآورد مدل لاجیت چندگانه کسری گردید. نتایج مطالعه نشان داد افزایش دما سهم سطح زیرکشت غلات و محصولات جالیزی را افزایش و سهم سطح زیرکشت حبوبات را کاهش می-دهد. لذا با توجه به پیش بینی های صورت گرفته در مورد افزایش دما در سال های آتی، انتظار بر این است که میزان کشت غلات افزایش و میزان کشت حبوبات کاهش یابد. بارش متغیر دیگری است که با افزایش آن سهم سطح زیرکشت غلات افزایش و سهم سایر انواع محصولات کاهش می-یابد. درصد رطوبت بر سهم سطح زیرکشت سبزیجات و محصولات صنعتی و سرعت باد نیز بر سهم سطح زیرکشت محصولات صنعتی و غلات موثر می باشد. از این رو توصیه می گردد نحوه واکنش تولیدکنندگان محصولات زراعی سالانه به تغییرات آب و هوایی تحت سناریوهای گوناگون پیش بینی و با مقایسه مقدار تولید بالقوه با نیازهای غذایی جامعه در آینده و تعیین شکاف های موجود، مبنای سیاست گذاری های لازم در این زمینه فراهم شود. همچنین با توجه به اینکه مطالعه حاضر تنها تخصیص زمین بین انواع محصولات سالانه زراعی را مدنظر قرار داده است، توصیه می-گردد مطالعات دیگری نیز در زمینه بررسی نحوه اثرگذاری تغییرات آب و هوایی بر تولیدات سایر بخش های کشاورزی از قبیل محصولات باغی و دامی صورت گیرد.

The most commonly used variant of conjoint analysis is choice-based conjoint (CBC). Here, hierarchical Bayesian (HB) multinomial logit (MNL) models are widely used for preference estimation at the individual respondent level. A new and very flexible approach to address multimodal and skewed preference heterogeneity in the context of CBC is the Dirichlet Process Mixture (DPM) MNL model. The number and masses of components do not have to be predisposed like in the latent class (LC) MNL model or in the mixture-of-normals (MoN) MNL model. The aim of this Monte Carlo study is to evaluate the performance of Bayesian choice models (basic MNL, HB-MNL, MoN-MNL, LC-MNL and DPM-MNL models) under varying data conditions (especially under multimodal heterogeneity structures) using statistical criteria for parameter recovery, goodness-of-fit and predictive accuracy. The core finding from this Monte Carlo study is that the standard HB-MNL model appears to be highly robust in multimodal preference settings.

Indonesia has set an ambitious target of reducing its maternal mortality rate to 125 maternal deaths per 100,000 live births by the year 2010. This poses formidable challenges in a geographically diverse country where the majority of births occur at home. One option for the Indonesian government in order to reduce its maternal mortality would be to increase rates of skilled assistance for home deliveries. In order to design appropriate policies to achieve this, it is imperative to understand the determinants of use of birth attendants by mothers delivering at home. We use the Andersen Behavioral Model as a theoretical framework to understand the determinants of the use of a trained provider, traditional birth attendant, or no trained assistance during home deliveries in Indonesia. The 1997 Indonesia Demographic and Health Survey (IDHS) was used, and data from the most recent home delivery was abstracted for analysis. Out of a total sample of 10,692 home deliveries, a majority (53%) used the services of a TBA, 40% had a doctor, nurse or midwife in attendance, and only 7% delivered with the help of family and/or friends. A multinomial logit model was used to predict determinants of use. Our results indicate that maternal education, religion, asset index quartile and number of antenatal visits are significant determinants among all choice sets.

The multinomial logit (MNL) model and its variations have been dominating the travel mode choice modeling field for decades. Advantages of the MNL model include its elegant closed-form mathematical structure and its interpretable model estimation results based on random utility theory, while its main limitation is the strict statistical assumptions. Recent computational advancement has allowed easier application of machine learning models to travel behavior analysis, though research in this field is not thorough or conclusive. In this paper, we explore the application of the extreme gradient boosting (XGB) model to travel mode choice modeling and compare the result with an MNL model, using the Delaware Valley 2012 regional household travel survey data. The XGB model is an ensemble method based on the decision-tree algorithm and it has recently received a great deal of attention and use because of its high machine learning performance. The modeling and predicting results of the XGB model and the MNL model are compared by examining their multi-class predictive errors. We found that the XGB model has overall higher prediction accuracy than the MNL model especially when the dataset is not extremely unbalanced. The MNL model has great explanatory power and it also displays strong consistency between training and testing errors. Multiple trip characteristics, socio-demographic traits, and built-environment variables are found to be significantly associated with people’s mode choices in the region, but mode-specific travel time is found to be the most determinant factor for mode choice.

Discrete choice experiments have proven useful in areas such as marketing, government planning, medical studies and psychological research, to help understand consumer preferences. To aid in these experiments, several groups of authors have contributed to the theoretical development of D-optimal and A-optimal discrete choice designs under the multinomial logit (MNL) model. In the setting in which the class of feasible designs is too large for complete search, Sun and Dean (J Stat Plann Inference 170:144–157, 2016) proposed a construction method for A-optimal designs for estimating a set of orthonormal contrasts in the option utilities via a linearization of the MNL model. In this paper, we show that the set of A-optimal designs that result from this linearization may or may not include the optimal design under the MNL model itself. We provide an alternative linearization that leads to an information matrix which coincides with that under the MNL model and, consequently, selects the same set of designs as being A-optimal. We obtain a bound for the average variance of a set of contrasts of interest under the MNL model, and show that the construction method of Sun and Dean (2016) can be used to identify A-optimal and A-efficient designs under the MNL model for both equal and unequal utilities.

Multinomial logit (MNL) models have been applied extensively in transportation engineering, marketing, and recreational demand modeling. Thus far, this type of model has not been used to estimate the proportion of crashes by collision type. This study investigated the applicability of MNL models to predict the proportion of crashes by collision type and to estimate crash counts by collision type. MNL models were compared with two other methods described in recent publications to estimate crash counts by collision type: (a) fixed proportions of crash counts for all collision types and (b) collision type models. This study employed data collected between 2002 and 2006 on crashes that occurred on rural, two-lane, undivided highway segments in Minnesota. The study results showed that the MNL model could be used to predict the proportion of crashes by collision type, at least for the data set used. Furthermore, the method based on the MNL model was found useful to estimate crash counts by collision type, and it performed better than the method based on the use of fixed proportions. The use of collision type models, however, was still found to be the best way to estimate crash counts by specific collision type. In cases where collision type models are affected by the small sample size and a low sample-mean problem, the method based on the MNL model is recommended.

Understanding and forecasting complex freight mode choice behavior under various industry, policy, and technology contexts is essential for freight planning and policymaking. Numerous models have been developed to provide insights into freight mode selection; most use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables despite potential nonlinear relationships in the data. A common challenge for researchers is the absence of a heuristic and efficient method to discern and define these complex relationships in logit model specifications. This often results in models that might be deficient in both predictive power and interpretability. To bridge this gap, we develop an MNL model for freight mode choice using the insights from machine learning (ML) models. ML models can better capture the nonlinear nature of many decision-making processes, and recent advances in “explainable AI” have greatly improved their interpretability. We showcase how interpretable ML methods help enhance the performance of MNL models and deepen our understanding of freight mode choice. Specifically, we apply SHapley Additive exPlanations (SHAP) to identify influential features and complex relationships to improve the MNL model’s performance. We evaluate this approach through a case study for Austin, Texas, where SHAP results reveal multiple important nonlinear relationships. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model. Findings from this study can be used to guide freight planning and inform policymakers about how key factors affect freight decision-making.

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.

Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

As essential infrastructure, high-speed rail (HSR) and air transport (AT) play crucial roles in socioeconomic development. With their continuous expansion in China, the overlap of HSR and AT networks has increased, providing travelers with more choices for intercity travel. Because fierce competition in the medium-to-long-distance segment affects the market share and transport capacity dispatching, the travel choice between HSR and AT has been of intense interest. This study utilized a unique fusion dataset collected from two separate organizations to conduct an empirical analysis of the travel mode choice behaviors of individuals when choosing between HSR and AT. A multinomial logit (MNL) model was adopted to examine the influences of key factors on passenger choice preferences. The results showed that the fitting effect of the MNL model was satisfactory, and the parameters were strongly interpretable. The McFadden Pseudo R 2 with a city-pair fixed effect in the MNL model increased by 17.3% compared with that without the city-pair fixed effect. All the related explanatory variables, including the trip distance by high-speed train, demography, ticket purchasing, and travel behavior characteristics, had significant positive effects on the passengers’ choice of AT, with trip distance having the largest effect. According to the parameter estimation, 1,160 km was the division for individual choice between HSR and AT. This study also compared the prediction accuracies of the MNL model and eight classical machine-learning models and found that random forest had the best performance. This study provides a new framework for analyzing travel choice modeling when choosing between HSR and AT.

In mathematics you don’t understand things. You just get used to them. (John von Neumann, 1903–57) Introduction The majority of practical choice study applications do not progress beyond the simple multinomial logit (MNL) model discussed in previous chapters. The ease of computation, and the wide availability of software packages capable of estimating the MNL model, suggest that this trend will continue. The ease with which the MNL model may be estimated, however, comes at a price in the form of the assumption of Independence of Identically Distributed (IID) error components. While the IID assumption and the behaviorally comparable assumption of Independence of Irrelevant Alternatives (IIA) allow for ease of computation (as well as providing a closed form solution), as with any assumption violations both can and do occur. When violations do occur, the cross-substitution effects (or correlation) observed between pairs of alternatives are no longer equal given the presence or absence of other alternatives within the complete list of available alternatives in the model (Louviere et al . 2000). The nested logit (NL) model represents a partial relaxation of the IID and IIA assumptions of the MNL model. As discussed in Chapter 4, this relaxation occurs in the variance components of the model, together with some correlation within sub-sets of alternatives, and while more advanced models such as mixed multinomial logit (see Chapter 15) relax the IID assumption more fully, the NL model represents an excellent advancement for the analyst in terms of studies of choice. As with the MNL model, the NL model is relatively straightforward to estimate and offers the added benefit of being a closed-form solution. More advanced models relax the IID assumption in terms of the covariances; however, all are of open-form solution and as such require complex analytical calculations to identify changes in the choice probabilities through varying levels of attributes (see Louviere et al . (2000) and Train (2003, 2009), as well as the following chapters in this book). In this chapter, we show how to use NLOGIT to estimate NL models and to interpret the output, especially the output that is additional to what is obtained when estimating an MNL model. As with previous chapters, we have been very specific in terms of our explanation of the command syntax as well as the output generated.

BackgroundMalaria has been a major public health problem in Nigeria and many other sub-Saharan African countries. Insecticide-treated nets have shown to be cost-effective in the prevention of malaria, but the number of people that actually use these nets has remained generally low. Studies that explore the determinants of use of ITN are desirable.MethodsStructured questionnaires based on thematic areas were administered by trained interviewers to 7,223 care-givers of under-five children selected from all the six geo-political zones of Nigeria. Bivariate analysis and multinomial logit model were used to identify possible determinants of use of ITN.ResultsBivariate analysis showed that under-five children whose care-givers had some misconceptions about causes and prevention of malaria were significantly less likely to use ITN even though the household may own a net (p < 0.0001). Education and correct knowledge about modes of prevention of malaria, knowing that malaria is dangerous and malaria can kill were also significantly associated with use of ITN (p < 0.0001). Knowledge of symptoms of malaria did not influence use of ITN. Association of non-use of ITN with misconceptions about prevention of malaria persisted with logistic regression (Odds ratio 0.847; 95% CI 0.747 to 0.960).ConclusionsMisconceptions about causes and prevention of malaria by caregivers adversely influence the use ITN by under-five children. Appropriate communication strategies should correct these misconceptions.

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and the customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Because all the utility parameters of the MNL model are unknown, the seller needs to simultaneously learn customers’ choice behavior and make dynamic decisions on assortments based on the current knowledge. The goal of the seller is to maximize the expected revenue, or, equivalently, to minimize the expected regret. Although dynamic assortment planning problem has received an increasing attention in revenue management, most existing policies require the estimation of mean utility for each product and the final regret usually involves the number of products [Formula: see text]. The optimal regret of the dynamic assortment planning problem under the most basic and popular choice model—the MNL model—is still open. By carefully analyzing a revenue potential function, we develop a trisection-based policy combined with adaptive confidence bound construction, which achieves an item-independent regret bound of [Formula: see text], where [Formula: see text] is the length of selling horizon. We further establish the matching lower bound result to show the optimality of our policy. There are two major advantages of the proposed policy. First, the regret of all our policies has no dependence on [Formula: see text]. Second, our policies are almost assumption-free: there is no assumption on mean utility nor any “separability” condition on the expected revenues for different assortments. We also extend our trisection search algorithm to capacitated MNL models and obtain the optimal regret [Formula: see text] (up to logrithmic factors) without any assumption on the mean utility parameters of items.

Analysis of driver injury severity in rural single-vehicle crashes

BackgroundWe investigate whether annotation of gene function can be improved using a classification scheme that is aware that functional classes are organized in a hierarchy. The classifiers look at phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and an MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome.ResultsThe results from all three models show substantial improvement over previous methods, which were based on the C5 decision tree algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining the three sources of information in this dataset, our new approach to combining data sources produces a higher accuracy rate than applying our models to each data source alone.ConclusionTogether, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information.