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In this paper, we address the problem of designing an experimental plan with both discrete and continuous factors under fairly general parametric statistical models. We propose a new algorithm, named ForLion, to search for locally optimal approximate designs under the D-criterion. The algorithm performs an exhaustive search in a design space with mixed factors while keeping high efficiency and reducing the number of distinct experimental settings. Its optimality is guaranteed by the general equivalence theorem. We present the relevant theoretical results for multinomial logit models (MLM) and generalized linear models (GLM), and demonstrate the superiority of our algorithm over state-of-the-art design algorithms using real-life experiments under MLM and GLM. Our simulation studies show that the ForLion algorithm could reduce the number of experimental settings by 25% or improve the relative efficiency of the designs by 17.5% on average. Our algorithm can help the experimenters reduce the time cost, the usage of experimental devices, and thus the total cost of their experiments while preserving high efficiencies of the designs. [ABSTRACT FROM AUTHOR]

We consider the newly developed multinomial mixed-link models for a high-risk intestinal metaplasia (IM) study with DNA methylation data. Different from the traditional multinomial logistic models commonly used for categorical responses, the mixed-link models allow us to select the most appropriate link function for each category. We show that the selected multinomial mixed-link model (Model 1) using the total number of stem cell divisions (TNSC) based on DNA methylation data outperforms the traditional logistic models in terms of cross-entropy loss from ten-fold cross-validations with significant p-values 8.12 × 10 − 4 and 6.94 × 10 − 5 . Based on our selected model, the significance of TNSC's effect in predicting the risk of IM is justified with a p-value less than 10 − 6 . We also select the most appropriate mixed-link models (Models 2 and 3) when an additional covariate, the status of gastric atrophy, is available. When the status is negative, mild, or moderate, we recommend Model 2; otherwise, we prefer Model 3. Both Models 2 and 3 can predict the risk of IM significantly better than Model 1, which justifies that the status of gastric atrophy is informative in predicting the risk of IM. [ABSTRACT FROM AUTHOR]

Japanese flounder Paralichthys olivaceus is one of the most important coastal fish species in Japan. However, its catch in the southwestern Sea of Japan has been decreasing. Therefore, there is a need to improve the accuracy of stock assessment. Catch-at-age, which is the basis for stock assessment, is calculated based on the length–frequency distribution using age-length keys. It is recommended that this basic information is revised annually. However, the information for this species has not been updated for a relatively long period. In this study, growth curves were estimated and compared with those recorded from other areas. A multinomial logistic age-length key was generated and compared with the classical catch-at-age calculation method to examine potential errors. On the basis of the results obtained, a sex-specific von Bertalanffy growth curve was selected using the Bayesian information criterion. A model of sex and season was chosen for the multinomial logistic age-length key. The relative error was smaller and the estimated age composition was more accurate with the multinomial logistic age-length key, even when data with missing values were used. The improved accuracy of catch-at-age achieved due to the superior characteristics of the multinomial logistic age-length key is expected to be useful for the advancement of stock assessment. [ABSTRACT FROM AUTHOR]

This study attempts to examine the influential factors causing under-5 child mortality (U5CM) in Hill Tracts of Chattogram division, Bangladesh, with a view to providing recommendations for redressing its ratio. It finds that the death of male children is 52.5% while the death of female children is 47.5%. For families which are financially troubled, pneumonia or other respiratory diseases are the prominent causes of death [19%]. The lack of sanitary facilities is another vital factor behind pneumonia mortality [22%]. About 24% of deaths in urban areas occur due to respiratory diseases or pneumonia, while 25% are ascribed to other causes. The chi-square test explores that there is a positive association between under-5 mortality in above mentioned covariates such as, residence, level of education, economic status, birth attendant, and sanitation facility except the mother’s education and the water resource. The multinomial logistic regression analysis shows that sanitation facilities, economic status, and birth attendants are significant to U5CM. There is a significant relationship between pneumonia or respiratory and economic status. Moreover, sanitation facility is associated with malnutrition. The study results reveal that several socioeconomic, demographic, and health-related variables affect these causes of death. [ABSTRACT FROM AUTHOR]

We consider the newly developed multinomial mixed-link models for a high-risk intestinal metaplasia (IM) study with DNA methylation data. Different from the traditional multinomial logistic models commonly used for categorical responses, the mixed-link models allow us to select the most appropriate link function for each category. We show that the selected multinomial mixed-link model (Model 1) using the total number of stem cell divisions (TNSC) based on DNA methylation data outperforms the traditional logistic models in terms of cross-entropy loss from ten-fold cross-validations with significant p-values 8.12×10−4 and 6.94×10−5. Based on our selected model, the significance of TNSC’s effect in predicting the risk of IM is justified with a p-value less than 10−6. We also select the most appropriate mixed-link models (Models 2 and 3) when an additional covariate, the status of gastric atrophy, is available. When the status is negative, mild, or moderate, we recommend Model 2; otherwise, we prefer Model 3. Both Models 2 and 3 can predict the risk of IM significantly better than Model 1, which justifies that the status of gastric atrophy is informative in predicting the risk of IM.

AbstractWheat is one of the staple crops widely cultivated in Arsi Zone of Ethiopia. It is the source of food and provides income for the majority of smallholder farmers. To this end, to benefit smallholders from their produce identifying and selecting the appropriate market is crucial. However, choosing a suitable market channel is not an easy task because there are different factors that influence market outlet choices. Hence, this study scrutinized the determinants of market outlet choice of smallholder wheat producers in Arsi Zone, Oromia National Regional State, Ethiopia. It uses primary and secondary data collected through questionnaires and desk reviews from 336 randomly selected households. Data were analyzed using multinomial logistic regression, percentage, and cross-tabulations of frequency distribution. The result from the multinomial logistic regression specifies that among the ten explanatory variables included in the model seven of them were statistically significant at 1%, 5%, and 10%. Those factors significantly affecting wheat producers’ market outlet choices were sex, age, education, year of participation, nearby market, nearby road and quantity produced. Based on the results, the study recommended that the government and stakeholders should focus on strengthening education, expansion of road infrastructure, telecom, and access to credit which promote farmers profitable market outlet choices in one hand and increases the production and productivity of wheat in another way.

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios, including variable screening, model selection, order selection for response categories, and variable selection. We perform our procedure on high-dimensional gene expression data with 801 patients, 2426 genes, and five types of cancerous tumors. As a result, we recommend three finalized models: one with 74 genes achieves extremely low cross-entropy loss and zero predictive error rate based on a five-fold cross-validation; and two other models with 31 and 4 genes, respectively, are recommended for prognostic multi-gene signatures. [ABSTRACT FROM AUTHOR]

Map matching of trajectory data has wide applications in path planning, traffic flow analysis, and intelligent driving. The process of map matching involves matching GPS trajectory points to roads in a roadway network, thereby converting a trajectory sequence into a segment sequence. However, GPS trajectories are frequently incorrectly matched during the map-matching process, leading to matching errors. Considering that few studies have focused on the causes of map-matching errors, as well as the distribution of these errors, the study aims to investigate the spatiotemporal characteristics and the contributing factors that cause map-matching errors. The study employs the Hidden Markov Model (HMM) algorithm to match the trajectories and identifies the four types of map-matching errors by examining the relationship between the matched trajectories and the driving routes. The map-matching errors consist of Off-Road Error (ORE), Wrong-match on Road Error (WRE), Off-Junction Error (OJE), and Wrong-match in Junction Error (WJE). The kernel density method and multinomial logistic model are further exploited to analyze the spatiotemporal patterns of the map-matching errors. The results indicate that the occurrence of map-matching errors substantially varies in time and space, with variation significantly influenced by intersection features and road characteristics. The findings provide a better understanding of the contributing factors associated with map-matching errors and serve to improve the accuracy of map matching for commercial vehicles. [ABSTRACT FROM AUTHOR]

Rural livelihood diversification has become a central policy in rural development and poverty alleviation. Farmworkers in irrigation schemes are a typical poor sub-group that has received little attention in research regarding their livelihood status and the contribution of diversification. This paper provides evidence derived from a study that was carried out to examine whether farmworkers in the study area diversify their livelihoods and identify their choices of livelihood-diversification strategies and the determinants thereof. Data were collected from 191 randomly selected farmworkers and analyzed using the binary probit model and the multinomial logistic model. Variables considered in the binary probit model—gender, number of dependents, employment type, farming experience, land leasing, and market access—influenced livelihood diversification among irrigation farmworkers in a statistically significant way. The multinomial logistic model analysis considered age, marital status, household dependents, leasing land from employer, farming experience, agricultural training, and access to markets as the statistically significant factors that influence the choice of livelihood-diversification strategies in the study area. The study concluded that rural development policies should consider leasing land to irrigation farmworkers that is adequate to reduce poverty, achieve food security, and sustain the livelihoods of farmworkers in the study area, as it boosts the generation of income. [ABSTRACT FROM AUTHOR]

Map matching of trajectory data has wide applications in path planning, traffic flow analysis, and intelligent driving. The process of map matching involves matching GPS trajectory points to roads in a roadway network, thereby converting a trajectory sequence into a segment sequence. However, GPS trajectories are frequently incorrectly matched during the map-matching process, leading to matching errors. Considering that few studies have focused on the causes of map-matching errors, as well as the distribution of these errors, the study aims to investigate the spatiotemporal characteristics and the contributing factors that cause map-matching errors. The study employs the Hidden Markov Model (HMM) algorithm to match the trajectories and identifies the four types of map-matching errors by examining the relationship between the matched trajectories and the driving routes. The map-matching errors consist of Off-Road Error (ORE), Wrong-match on Road Error (WRE), Off-Junction Error (OJE), and Wrong-match in Junction Error (WJE). The kernel density method and multinomial logistic model are further exploited to analyze the spatiotemporal patterns of the map-matching errors. The results indicate that the occurrence of map-matching errors substantially varies in time and space, with variation significantly influenced by intersection features and road characteristics. The findings provide a better understanding of the contributing factors associated with map-matching errors and serve to improve the accuracy of map matching for commercial vehicles.

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios, including variable screening, model selection, order selection for response categories, and variable selection. We perform our procedure on high-dimensional gene expression data with 801 patients, 2426 genes, and five types of cancerous tumors. As a result, we recommend three finalized models: one with 74 genes achieves extremely low cross-entropy loss and zero predictive error rate based on a five-fold cross-validation; and two other models with 31 and 4 genes, respectively, are recommended for prognostic multi-gene signatures.

After 2020, poverty alleviation in China will shift from eliminating absolute poverty to alleviating unbalanced and insufficient relative poverty. Based on Amartya Sen's capability approach, this article aimed to realize the freedom of "capability" of children and ensure the well-being and development of Chinese adolescents, who are often ignored in child poverty research. The study sought to estimate the 10–15-year-olds in a multidimensional capabilities poverty group. Our analysis was based on the adolescent capability methodology framework, using data from the 2018 China Family Panel Studies (CFPS) to investigate the types and influencing factors of adolescent capability poverty in China. The result of latent class analysis showed that there are four types of capability poverty among the Chinese adolescents, namely, Extreme Capability Poverty Class, Goal Capability Poverty Class, Opportunity Capability Poverty Class, and Capability Non-Poverty Class. Multinomial logistic regression showed that the personal factors of gender, ethnic minority, hukou, and pocket money; the family factors of parents' marital status, parents' education level, and region of residence; and the family economic factor of per capita family income had a significant impact on the types of China's adolescent capability poverty. The article provides empirical and quantitative evidence for the adolescent (ages 10–15) class of capability poverty. The findings provide a reference for policy makers to target poverty-alleviation efforts according to different types of poverty and to interrupt the intergenerational transmission of poverty. [ABSTRACT FROM AUTHOR]

Depending on their place of residence, older persons have unequal access to long-term care (LTC) services. This article investigates how the county-level supply of inpatient and outpatient LTC services influences individual-level LTC choices of older persons. Administrative data on LTC service supply from the German Care Statistic are combined with representative survey data on the LTC choices of N = 1303 persons aged 80+ from the German Federal State North Rhine-Westphalia. Random utility models are applied to model the choice among three care arrangements: receiving inpatient care in an institutional setting (e.g., nursing home), receiving outpatient care in the community, and living in the community without receiving inpatient or outpatient care. The main findings are: Higher inpatient service supply increases the probability that older persons leave the community and enter institutional LTC. Higher outpatient service supply increases the probability that older persons choose to receive outpatient care in the community instead of entering institutional LTC. The results suggest that policy makers must consider the county-level LTC service supply when designing equitable LTC systems that meet the needs of older persons in a cost-effective way. [ABSTRACT FROM AUTHOR]

The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were originally proposed to characterize accuracy improvement in predicting a binary outcome, when new biomarkers are added to regression models. These two indices have been extended from binary outcomes to multi-categorical and survival outcomes. Working on an AIDS study where the onset of cognitive impairment is competing risk censored by death, we extend the NRI and the IDI to competing risk outcomes, by using cumulative incidence functions to quantify cumulative risks of competing events, and adopting the definitions of the two indices for multi-category outcomes. The "missing" category due to independent censoring is handled through inverse probability weighting. Various competing risk models are considered, such as the Fine and Gray, multistate, and multinomial logistic models. Estimation methods for the NRI and the IDI from competing risk data are presented. The inference for the NRI is constructed based on asymptotic normality of its estimator, and the bias-corrected and accelerated bootstrap procedure is used for the IDI. Simulations demonstrate that the proposed inferential procedures perform very well. The Multicenter AIDS Cohort Study is used to illustrate the practical utility of the extended NRI and IDI for competing risk outcomes. [ABSTRACT FROM AUTHOR]

Abstract Background Agriculture is considered an important strategy for overcoming many of the emergencies faced by rural households in developing countries. In rural Ethiopia household access to food largely depends on what the household grows, either because they consume what they grow, or they purchase food with the income earned from what they grow. This study examines effect of crop diversification on food security and determinants of household food security among rural farm households of Sinana District, Oromia Regional state. The study uses a multi stage sampling procedure to select 384 sample households. Data were collected using a household survey, a focus group discussant (FGD), and key informant interviews. Ordinary least squares regression (OLS) and multinomial logistic regression model were used to analyze the data collected. Results We found that crop diversification had a positive and significant effect on household food security. Although crop diversification was positively associated with household food security, several other factors were found to be as or more important in increasing household food security. The education of the household head, access to irrigation system, livestock owned, total income, and remittance positively affected household food security. In contrast, age of household head and distance to nearest market were negatively associated with food security. Conclusions The study conclude that any effort to increase household food security should consider empowerment of farmers through adequate training and informal education, enhancing crop diversification, strengthening the rural infrastructural development (roads, market centers, and cooperatives).

BackgroundMulti-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved outside institutions due to privacy or other concerns. Such decomposition makes it possible to conduct grid computing to protect the privacy of individual observations.MethodsThis paper proposes two grid multi-category response models for ordinal and multinomial logistic regressions. Grid computation to test model assumptions is also developed for these two types of models. In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation.ResultsSimulation results show that the grid models produce the same results as those obtained from corresponding centralized models, demonstrating that it is possible to build models using multi-center data without losing accuracy or transmitting observation-level data. Two real data sets are used to evaluate the performance of our proposed grid models.ConclusionsThe grid fitting method offers a practical solution for resolving privacy and other issues caused by pooling all data in a central site. The proposed method is applicable for various likelihood estimation problems, including other generalized linear models.

The importance of cassava in the food system of Ghanaians cannot be underestimated. Research and development systems have released 25 new cassava varieties. The adoptions of these varieties have been low. The study explores factors that induce farmers to use improved cassava varieties in Ghana. The study used data from 450 cassava farmers and employed multinomial logistic model. The results revealed that apart from the farm and farmer characteristics such as farm size, distance, age and gender, perception of cassava characteristics had strong influence on the use of improved cassava varieties. Perception of disease resistance, perception of longevity in-soil and high yielding had effect on adoption of improved cassava varieties. Institutional factors including awareness, field demonstrations and extension visit significantly affected adoption. The importance of strengthening and resourcing research institutions and extension services to promote and create awareness about the existing improved cassava varieties is vital.

Background: The main goal of this study was to diagnose the two most common thyroid disorders, namely, hyperthyroidism and hypothyroidism, based on multinomial logistic regression and neural network models. In addition, the study evaluated the predictive ability of laboratory tests against the individual clinical symptoms score. Materials and Methods: In this study, the data from patients with thyroid dysfunction who referred to Imam Khomeini Clinic and Shahid Beheshti Hospital in Hamadan were collected. The data contained 310 subjects in one of three classes—euthyroid, hyperthyroidism, and hypothyroidism. Collected variables included demographics and symptoms of hypothyroidism and hyperthyroidism, as well as laboratory tests. To compare the predictive ability of the clinical signs and laboratory tests, different multinomial logistic regression and neural network models were fitted to the data. These models were compared in terms of the mean of the accuracy and area under the curve (AUC). Results: The results showed better performance of neural network model than multinomial logistic regression in all cases. The best predictive performance for logistic regression (with a mean accuracy of 91.4%) and neural network models (with a mean accuracy of 96.3%) was when all variables were included in the model. In addition, the predictive performance of two models based on symptomatic variables was superior to laboratory variables. Conclusions: Both neural network and logistic regression models have a high predictive ability to diagnose thyroid disorder, although neural network performance is better than logistic regression. In addition, as achieving less error prediction model has always been a matter of concern for researchers in the field of disease diagnosis, predictive nonparametric techniques, such as neural networks, provide new opportunities to obtain more accurate predictions in the field of medical research.

Rural livelihood diversification has become a central policy in rural development and poverty alleviation. Farmworkers in irrigation schemes are a typical poor sub-group that has received little attention in research regarding their livelihood status and the contribution of diversification. This paper provides evidence derived from a study that was carried out to examine whether farmworkers in the study area diversify their livelihoods and identify their choices of livelihood-diversification strategies and the determinants thereof. Data were collected from 191 randomly selected farmworkers and analyzed using the binary probit model and the multinomial logistic model. Variables considered in the binary probit model—gender, number of dependents, employment type, farming experience, land leasing, and market access—influenced livelihood diversification among irrigation farmworkers in a statistically significant way. The multinomial logistic model analysis considered age, marital status, household dependents, leasing land from employer, farming experience, agricultural training, and access to markets as the statistically significant factors that influence the choice of livelihood-diversification strategies in the study area. The study concluded that rural development policies should consider leasing land to irrigation farmworkers that is adequate to reduce poverty, achieve food security, and sustain the livelihoods of farmworkers in the study area, as it boosts the generation of income.

After 2020, poverty alleviation in China will shift from eliminating absolute poverty to alleviating unbalanced and insufficient relative poverty. Based on Amartya Sen’s capability approach, this article aimed to realize the freedom of “capability” of children and ensure the well-being and development of Chinese adolescents, who are often ignored in child poverty research. The study sought to estimate the 10–15-year-olds in a multidimensional capabilities poverty group. Our analysis was based on the adolescent capability methodology framework, using data from the 2018 China Family Panel Studies (CFPS) to investigate the types and influencing factors of adolescent capability poverty in China. The result of latent class analysis showed that there are four types of capability poverty among the Chinese adolescents, namely, Extreme Capability Poverty Class, Goal Capability Poverty Class, Opportunity Capability Poverty Class, and Capability Non-Poverty Class. Multinomial logistic regression showed that the personal factors of gender, ethnic minority, hukou, and pocket money; the family factors of parents’ marital status, parents’ education level, and region of residence; and the family economic factor of per capita family income had a significant impact on the types of China’s adolescent capability poverty. The article provides empirical and quantitative evidence for the adolescent (ages 10–15) class of capability poverty. The findings provide a reference for policy makers to target poverty-alleviation efforts according to different types of poverty and to interrupt the intergenerational transmission of poverty.

This paper focuses on pragmatic competence development in second or foreign language learners. In particular, it attempts to fill the significant research gap in measuring change in pragmatic competence and capturing pragmalinguistic and sociopragmatic development over time. The paper proposes an innovative approach by applying a logistic model with multinomial distribution for measuring change in InterLanguage Pragmatics Research (ILP). Applied in the field of pragmatics, this statistical tool offers a comprehensive and flexible approach to modelling relations between independent and dependent variables in ILP research. The model is tested in a longitudinal study of Polish undergraduate students learning English, and specifically in the way they formulate requests by means of requestive directness strategies. The paper concludes that, regardless of time elapsing, the factors P (power distance) and D (social distance) have a highly significant influence on the use of requestive directness strategies by Poles learning EFL. Furthermore, the analysis indicates that the pragmatic output of Poles learning EFL is dependent on one more independent variable: the estimation of future social distance (F). [ABSTRACT FROM AUTHOR]

This paper focuses on pragmatic competence development in second or foreign language learners. In particular, it attempts to fill the significant research gap in measuring change in pragmatic competence and capturing pragmalinguistic and sociopragmatic development over time. The paper proposes an innovative approach by applying a logistic model with multinomial distribution for measuring change in InterLanguage Pragmatics Research (ILP). Applied in the field of pragmatics, this statistical tool offers a comprehensive and flexible approach to modelling relations between independent and dependent variables in ILP research. The model is tested in a longitudinal study of Polish undergraduate students learning English, and specifically in the way they formulate requests by means of requestive directness strategies. The paper concludes that, regardless of time elapsing, the factors P (power distance) and D (social distance) have a highly significant influence on the use of requestive directness strategies by Poles learning EFL. Furthermore, the analysis indicates that the pragmatic output of Poles learning EFL is dependent on one more independent variable: the estimation of future social distance (F). [ABSTRACT FROM AUTHOR]

The importance of cassava in the food system of Ghanaians cannot be underestimated. Research and development systems have released 25 new cassava varieties. The adoptions of these varieties have been low. The study explores factors that induce farmers to use improved cassava varieties in Ghana. The study used data from 450 cassava farmers and employed multinomial logistic model. The results revealed that apart from the farm and farmer characteristics such as farm size, distance, age and gender, perception of cassava characteristics had strong influence on the use of improved cassava varieties. Perception of disease resistance, perception of longevity in-soil and high yielding had effect on adoption of improved cassava varieties. Institutional factors including awareness, field demonstrations and extension visit significantly affected adoption. The importance of strengthening and resourcing research institutions and extension services to promote and create awareness about the existing improved cassava varieties is vital. [ABSTRACT FROM AUTHOR]

Summary: The paper addresses the task of forming a prior distribution to represent expert opinion about a multinomial model that contains covariates. The task has not previously been addressed. We suppose that the sampling model is a multinomial logistic regression and represent expert opinion about the regression coefficients by a multivariate normal distribution. This logistic–normal model gives a flexible prior distribution that can capture a broad variety of expert opinion. The challenge is to find meaningful assessment tasks that an expert can perform and which should yield appropriate information to determine the values of parameters in the prior distribution, and to develop theory for determining the parameter values from the assessments. A method is proposed that meets this challenge. The method is implemented in interactive easy‐to‐use software that is freely available. It provides a graphical interface that the expert uses to assess quartiles of sets of proportions and the method determines a mean vector and a positive definite covariance matrix to represent the expert's opinions. The assessment tasks chosen yield parameter values that satisfy the usual laws of probability without the expert being aware of the constraints that this imposes. Special attention is given to feedback that encourages the expert to consider his or her opinions from a different perspective. The method is illustrated in an example that shows its viability and usefulness. [ABSTRACT FROM AUTHOR]

• A large retrospective survey was designed to assess travel behaviors before / after a workplace relocation. • Transitions in commuter satisfaction was assessed using a multinomial logistic model. • The effect of travel modes attitudes on commuter satisfaction is confirmed, before and after a workplace relocation. • Workplace satisfaction and workplace attachment have significant impact on commuter satisfaction. • The perception of changes in travel time is an important factor in commuting satisfaction changes. • The consideration of 'activity places' proves useful in explaining the commuting satisfaction. While attention has been paid to travel satisfaction studies during the last decade, less research has investigated the relationship between the influence of a sense of place – and more particularly the psycho-environmental concepts of workplace attachment and workplace satisfaction – and commuting satisfaction. Using a retrospective survey, this paper focuses on the relocation of a workplace within the city of Montréal (Canada); the McGill University Healthcare Centre. It examines the motivations and barriers involved in the evolution of commuting characteristics, using the theoretical framework of mobility biographies. The empirical analysis includes a large dataset of independent variables, applying a multinomial logistic model in order to explain the transition of the employees' commuting satisfaction. With regard to the measurement of change in commuting satisfaction, the findings confirm the role of attitudes, while taking into account the contextual differences before and after relocation. The change in travel time is found to be an important factor in the evolution of commuter satisfaction, whereby reduced commuting time improves satisfaction, as expected. In addition, the more an employee is attached to the new workplace, the more the commuting satisfaction will increase. The results provide further evidence that the meaning of the workplace goes beyond a location or a space, and opens up new avenues for research in travel behavior. [ABSTRACT FROM AUTHOR]

Demand-side response (DSR), the incentivised time-shifting of energy use by consumers away from peak times, is regarded as a potentially effective measure to balance electricity supply and demand. This will be even more important in the low-carbon energy system of the future, with a high share of non-dispatchable power, such as variable renewable energy and nuclear power. Most DSR programmes require consumers' active engagement in shifting end-use activities. Previous studies have, however, rarely revealed socio-demographic factors influential for consumers' willingness-to-shift specific end-use activities. This study thus aims to fill this research gap and, using a multinomial logistic model to analyse a nationwide survey, identify factors influential for DSR-related decisions. The nationwide survey for 1004 respondents was carried out to collect data about consumers' willingness-to-shift their daily activities. We focused on the activities that constitute the major part of domestic energy consumption, i.e. cooking, dish-washing, entertainment, heating, laundry and showering. According to the results, consumers' original timing of the end-use activities, socio-demographic factors, ownership of specific appliances and level of concern for energy-saving are influential for their willingness-to-shift activities. These findings can not only help policymakers make more targeted DSR promotion plans but also help to improve broader modelling tools to better consider consumers' willingness-to-shift their demand. [ABSTRACT FROM AUTHOR]

Background: The main goal of this study was to diagnose the two most common thyroid disorders, namely, hyperthyroidism and hypothyroidism, based on multinomial logistic regression and neural network models. In addition, the study evaluated the predictive ability of laboratory tests against the individual clinical symptoms score. Materials and Methods: In this study, the data from patients with thyroid dysfunction who referred to Imam Khomeini Clinic and Shahid Beheshti Hospital in Hamadan were collected. The data contained 310 subjects in one of three classes—euthyroid, hyperthyroidism, and hypothyroidism. Collected variables included demographics and symptoms of hypothyroidism and hyperthyroidism, as well as laboratory tests. To compare the predictive ability of the clinical signs and laboratory tests, different multinomial logistic regression and neural network models were fitted to the data. These models were compared in terms of the mean of the accuracy and area under the curve (AUC). Results: The results showed better performance of neural network model than multinomial logistic regression in all cases. The best predictive performance for logistic regression (with a mean accuracy of 91.4%) and neural network models (with a mean accuracy of 96.3%) was when all variables were included in the model. In addition, the predictive performance of two models based on symptomatic variables was superior to laboratory variables. Conclusions: Both neural network and logistic regression models have a high predictive ability to diagnose thyroid disorder, although neural network performance is better than logistic regression. In addition, as achieving less error prediction model has always been a matter of concern for researchers in the field of disease diagnosis, predictive nonparametric techniques, such as neural networks, provide new opportunities to obtain more accurate predictions in the field of medical research. [ABSTRACT FROM AUTHOR]

Recent contributions to the theoretical and empirical trade literature underscore the channels by which exporting occurs, either through increasing the intensity of existing trade flows or by establishing new trade relationships. However, less is known about the extent to which trade liberalization influences the likelihood of trade along these channels. In this paper we develop a multinomial logit model to assess how tariff changes on agri-food imports affect the probability that country-commodity pairs will enter, exit, or maintain a presence in the U.S. agri-food import market. Using detailed bilateral tariff and trade data between 1996 and 2006, the results suggest that while U.S. tariff reductions provide a small but statistically significant increase on the probability of maintaining existing trade relationships, the magnitude of the impact on new exports is twice as large as the impact on continuously traded goods and the likelihood of disappearing products. The results have important policy implications regarding the channels by which imports change in conjunction with changes in U.S. agri-food import tariffs.

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The emergence of free-floating bike sharing (FFBS) has made essential changes in urban bicycle travel. In order to explore the differences of travel behavior among private bicycle (PB), public bicycle sharing (PBS) and FFBS, a survey was conducted in Kunming, China in 2018. Firstly, the differences of travel characteristics among these three modes were analyzed based on 522 valid questionnaires. Then, a multinomial logistic model was applied to explore the influential factors among them. The results show that: 1) PB and FFBS are more attractive for long-distance travel enthusiasts compared with PBS, PB is rarely used for transfer in suburbs while FFBS is the most desirable in connecting other travel modes. The theft problem and high maintenance costs are main obstacles for PB. 2) The elderly, high-income and non-student groups in inflexible travel activities show a preference for PBS, while the young, low-income and student groups in flexible travel demands have a tendency towards FFBS. 3) PB and PBS imped the use of FFBS to a certain extent, while PB seems to have less impact on PBS. 4) FFBS is desirable in temporary travel demand while PBS is more preferable in fixed demand. Interestingly, residents with registered permanent residence tend to use PBS while those without registered permanent residence prefer FFBS. Finally, several management strategies and policy recommendations were proposed for the government and FFBS enterprises to improve the management of bicycles. [ABSTRACT FROM AUTHOR]

China is a country with a unique history of gender traditions and birth-control regulations. Traditional gender role attitudes, closely related to son preference, exert entrenched influence on the fertility intentions and behaviours of women in China. In recent years, despite the fact that employment characteristics and educational attainment of women have caught up with men, China has witnessed a resurgence of patriarchal Confucian tradition. Gender relations in the private sphere are increasingly regulated by traditional gender norms. Considering the declining fertility in China and recent fertility policy adjustments, this paper analyses the varying effects of gender role attitudes on fertility intentions of women under different birth control policies, utilising data from 1,422 questionnaires conducted in 2015 in the Shaanxi Province in northwestern China and using the Multinomial Logistic Model. Our findings imply that the relationship between women's gender role attitudes and fertility intention differs between women affected by different birth control policies. We find that among married women with one child who are restricted to one child, the more egalitarian the gender role attitude, the more likely they intend to have a second child. Among married women who have had one child and are allowed to have two children, the more egalitarian the gender role attitude, the less likely they intend to have a second child. The results also indicate that traditional gender role attitudes and norms still play an important role in Chinese women's fertility intentions. [ABSTRACT FROM AUTHOR]

In this study we aim at assessing the willingness to invest of small-apple farms in Korça region-Albania. Furthermore, we want to identify and assess what are major determinants, as percept by farmers', willingness to invest. To this purpose we used data collected through face-to-face interviews with apple farmers. We used descriptive statistics, as well as classical and multinomial logistic regression. Most of farmers are willing to invest in view of their positive expectations for investment climate. Other important factors of willingness to invest are access to loans, advisory extension services, market competition, willingness to cooperate and willingness to take loans, and farm income, while socio-demographic factors such as age, education, and farming experience do not have significant effect on willingness to invest. Large farms are generally more willing to invest. We present also a general framework of constraints and policy levers to improving the investment climate which is almost valid also for Albania. Improving farmers' access to private credit sources, policy stability, providing information and public advisory services to small farmers, support farmers to adopt new technology, support to the creation of farmers' formal and informal groups, and mitigate risks of various type are some but essential policy measures to motivate farmers to invest mor e in apple. [ABSTRACT FROM AUTHOR]

Purpose: The study seeks to gain an understanding about consumer's mobile app-based shopping and develop appropriate studies for marketers to use the mobile applications as a better tool of selling over the brick-and-mortar shops. Design/methodology/approach: For the purpose of gathering primary data, a survey was conducted with the help of a structured questionnaire. The data was collected from 1498 buyers. The Multinomial Logistic Regression Model was used to examine the factors which determine consumers' response to mobile appbased shopping. The demographic variables considered for the study are: age, gender, occupation, and income. Findings: The study examined gender, age, income and occupational differences with respect to the preference of this medium of shopping. It revealed that the perceived usefulness and the ease of use played a significant role in the use of mobile apps as the emerging shopping medium over the traditional brick-andmortar shops. Limitations: A major limitation of the study is that it is confined to Delhi and NCR region. So, though the study provides in-depth insights into customers' perceptions about mobile app-based shopping, these insights cannot be used to form a regional or national pattern of customer choices and preferences. Practical Implications: It is important to engage with consumers' interest by designing mobile apps which provides comfort, convenience, utility, cost-saving, and captivating content for all demographics of consumers', such as, younger and older groups and equally among the males and females. Originality/Value: An attempt has been made to construct a model by positioning perceived usefulness and the ease of use as the central components of the technology acceptance model (TAM) and studying the impact of demographic variables on the consumer's mobile app-based shopping. [ABSTRACT FROM AUTHOR]

This research explores and attempts to understand transit riders' behavioural responses towards real-time transit information for two specific situations: the presence of inconsistent information on transit service recovery and the effects of crowded trains during rush hours. A survey was designed and conducted to collect light rail transit (LRT) riders' behavioural responses in Calgary, Alberta. Multinomial logit models were developed and calibrated to explore the effects of the described scenarios on riders' responses. The results led to the conclusion that socioeconomic attributes, experience with advanced passenger information system (APIS) system, familiarity with public transit in general and Calgary's LRT system in particular, and the characteristics of origin LRT stations had strong influences on travellers' behavioural responses. It was also determined that travellers' actions vary significantly depending on the purpose of the trip, time of the trip, and weather conditions. [ABSTRACT FROM AUTHOR]

COVID-19 measures implied many changes to travel behaviour and transport mode choice during the pandemic. This study seeks to understand what individual characteristics and travel attributes are related to transport mode choice before, during, and after the first lockdown in Italy. Based on an online survey (carried out in May 2020 in Milan), three multinomial regression models are presented. The results show that and in which measure parameters regarding distance and duration of daily travel are markedly related to transport mode before the lockdown. However, these factors are less significantly associated with the transport mode during and after the lockdown. Meanwhile, factors such as Preferences and Worry about using public transport have more significant relationship with the modal choice during the pandemic. Regarding individual characteristics, women are more likely to use active mode during and after the lockdown. Additionally, two personality traits of Agreeableness , and Openness to experiences are related to transport mode during and after the lockdown, respectively. Overall, this study reveals that in addition to socio-demographic factors, other variables such as worry about using public transport, preference, and personality are associated with the choice of transport mode during the lockdown. • Travel well-being is analysed by a survey carried out during Lock-down in March 2020 in Italy. • The survey was made up of three modules on socio-economical and general items, transportation and well-being subjects. • Non-linear regression models are applied to investigate the modal choice behaviour. • Results show that during the pandemic, subjective and objective factors affect differently on the choice of mobility. [ABSTRACT FROM AUTHOR]

For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. We propose the Bayesian calibration of computer model mixture method which relies on the idea of representing the real system output as a mixture of the available computer model outputs with unknown input dependent weight functions. The method builds a fully Bayesian predictive model as an emulator for the real system output by combining, weighting, and calibrating the available models in the Bayesian framework. Moreover, it fits a mixture of calibrated computer models that can be used by the domain scientist as a mean to combine the available computer models, in a flexible and principled manner, and perform reliable simulations. It can address realistic cases where one model may be more accurate than the others at different input values because the mixture weights, indicating the contribution of each model, are functions of the input. Inference on the calibration parameters can consider multiple computer models associated with different physics. The method does not require knowledge of the fidelity order of the models. We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework. We implement the proposed method in a real-world application involving the Weather Research and Forecasting large-scale climate model. [ABSTRACT FROM AUTHOR]

Environmental regulation violations (ERVs) result in pollution incidents and environmental degradation. Although different types of ERVs may have specific determinants, few studies have investigated how determinants are associated with different ERV types. This paper investigates region and firm level determinants of three types of violation: unlicensed facility (UF), improper operation (IO) and excessive emission (EE), based on 629 ERV cases reported in Chongqing, China during 2011–2014. At the firm level, a 10% increase in a firm's registered capital is associated with 0.4% less probability of UF and 0.3% less for IO violations, but 0.7% more for EE. Compared with private firms, firms with sole or collective ownership or joint ownership with non-Mainland China capital reduce the probability of UF violations by 15.3% and 14.3%. Joint ownership with non-Mainland China capital increases the probability of IO by 16.1%, while whole ownership of it decreases IO probability by 26.7%. Sole or collective ownership is associated with 27.4% increase of EE violation. At the regional level, a 10% increase in resident population, industry GDP and number of enterprise are significantly associated with higher probability of UF (7.9%, 5.5% and 5.3% respectively). Increased resident population and industry GDP are associated with 7.3% and 4.3% decrease in the probability of EE, while IO violations are not related with any region level factors except number of enterprises (−6.5%). Thus both firm level and region level factors have significant impacts on type-specific violations of environmental regulations, and these results will contribute to effective environmental management practices and cleaner production. [ABSTRACT FROM AUTHOR]

Recent contributions to the theoretical and empirical trade literature underscore the channels by which exporting occurs, either through increasing the intensity of existing trade flows or by establishing new trade relationships. However, less is known about the extent to which trade liberalization influences the likelihood of trade along these channels. In this paper we develop a multinomial logit model to assess how tariff changes on agri-food imports affect the probability that country-commodity pairs will enter, exit, or maintain a presence in the U.S. agri-food import market. Using detailed bilateral tariff and trade data between 1996 and 2006, the results suggest that while U.S. tariff reductions provide a small but statistically significant increase on the probability of maintaining existing trade relationships, the magnitude of the impact on new exports is twice as large as the impact on continuously traded goods and the likelihood of disappearing products. The results have important policy implications regarding the channels by which imports change in conjunction with changes in U.S. agri-food import tariffs. [ABSTRACT FROM AUTHOR]