Terms Of Model Fit Research Articles

Effect of Social Norm on Consumer Demand: Multiple Constraint Approach

The goal of the study is to understand the role of social norm in purchase decisions where demand is revealed in the form of multiple-discreteness. Consumers are socially engaged in various activities through the expectation from others in their community. Actions or decisions are likely to reflect this influence. This implicit or explicit social norm is revealed as the rules, regulations, and standards that are understood, shared, endorsed, and expected by group members. When consumers’ decisions are in distance from the norm, they come to face discomfort such as shame, guilt, embarrassment, and anxiety. These pressure act as a constraint as opposed to utility in their decision making. In this study, the effect of social norms on consumer demand is captured via multiple constraint model where constraints are not only from budget equation but also from psychological burden induced by the deviation from the norm. The posterior distributions of model parameters were estimated via conjoint study allowing for heterogeneity via hierarchical Bayesian framework. Individual characteristics such as age, gender and work experience are also used as covariates for capturing the observed heterogeneity. The empirical results show the role of social norm as constraint in consumers’ utility maximization. The proposed model accounting for social constraint outperforms the standard budget constraint-only model in terms of model fit. It is found that people with longer job experience tend to be more robust and resistant to the deviation from the norm. Incorporating social norm into the utility model allows for another means to disentangle the reason for no-purchase as ‘not preferred’ and ‘not able to buy’.

Evidence of Validity for a Newly Developed Digital Cognitive Test Battery.

Clinical practice still relies heavily on traditional paper-and-pencil testing to assess a patient’s cognitive functions. Digital technology has the potential to be an efficient and powerful alternative, but for many of the existing digital tests and test batteries the psychometric properties have not been properly established. We validated a newly developed digital test battery consisting of digitized versions of conventional neuropsychological tests. Two confirmatory factor analysis models were specified: a model based on traditional neuropsychological theory and expert consensus and one based on the Cattell-Horn-Carroll (CHC) taxonomy. For both models, the outcome measures of the digital tests loaded on the cognitive domains in the same way as established in the neuropsychological literature. Interestingly, no clear distinction could be made between the CHC model and traditional neuropsychological model in terms of model fit. Taken together, these findings provide preliminary evidence for the structural validity of the digital cognitive test battery.

A psychometric evaluation of the 17 itemed Utrecht Work Engagement Scale (UWES-17) in Uganda

Purpose - This study aimed at the establishment of the psychometric properties of the UWES-17 itemed factorial structure. This was done by examining the similarities and differences in terms of model fit of the tri-factor model to a one-factor model. Design/methodology/approach - Using a cross-sectional design, confirmatory factor analysis was used to evaluate the 17-item uni-dimensional and the 17-item tri-factor UWES respectively on a sample of 323 Education Assistants (professional teachers) in Uganda. Findings - The study confirmed an 11 item tri-factor UWES-Ug as a reliable and parsimonious factor structure within this cohort.Research limitations/implications - The sample was restricted to teachers and this limits the generalisability of the findings.Practical implications - On account of these results, the study sample evidently attests to the fact that work engagement is best represented as a tri-factor construct in Ugandan context. This study contributes to theory by confirmation of the three-factor structure of work engagement in developing countries through use of perceptual data from a Ugandan sample. Originality/value - This is a pioneer empirical study that validates the UWES 17 itemed scale in Uganda.

Comparative Analysis of Proposed Strategies for Incorporating Biologic Factors into Breast Cancer Staging

Tumor biology is an important prognostic factor in breast cancer. This study aimed to compare three staging systems incorporating both biologic factors and anatomic staging (AJCC 8th-edition pathologic prognostic staging, Bioscore, and Risk Score) in a large population-based cohort. The Surveillance, Epidemiology and End Results program was used to select patients with primary stages 1-4 breast cancer diagnosed in 2010. Patients with inflammatory carcinoma, those with missing data for biologic factors, and those with stages 1-3 disease not treated with surgery were excluded from the study. Estimates of 5-year disease-specific survival (DSS) were calculated using the Kaplan-Meier method. The Harrel concordance index (C-index) and the Akaike Information Criterion were used to compare each model in terms of predicting DSS. The study included 21,901 patients with a median age of 60years. The median follow-up period was 52months. All the staging models stratified DSS, with a stepwise decrease in DSS for each increase in risk category or score. The C-index of each model incorporating biologic factors was higher than the C-index for anatomic staging alone (C-index: 0.832 vs. 0.856 for AJCC pathologic prognostic staging, 0.856 for Bioscore, and 0.864 for Risk Score, all p < 0.001). The staging systems incorporating biologic factors did not differ significantly in terms of model fit. Staging systems incorporating biologic factors perform better than anatomic staging alone. Implementation of the AJCC 8th-edition pathologic prognostic staging was an important initial step in the inclusion of tumor biology in staging. Given its simplicity and ease of use, the Risk Score should be given consideration as an alternative staging system.

Accommodating correlation across days in multiple discrete-continuous models for time use

The MDCEV modelling framework has established itself as the preferred method for modelling time allocation, with data very often collected through travel or activity diaries. However, standard implementations fail to recognise the fact that many of these datasets contain information on multiple days for the same individual, with possible correlations and substitution between days. This paper discusses how the theoretical accommodation of these effects is not straightforward, especially with budget constraints at the day and multi-day level. We rely on additive utility functions where we accommodate correlation between activities at the within-day and between-day level using a mixed MDCEV model, with multivariate random distributions. We illustrate our approach using a well-known time use datasets, confirming our theoretical points and highlighting the benefits of allowing for correlation across days in terms of model fit and behavioural insights.

Testing a Syndemic Index of Psychosocial and Structural Factors associated with HIV Testing among Black Men.

Black populations in the United States are disproportionately affected by HIV. This disparity may be affected by social and structural barriers to HIV testing, leading to undiagnosed infection and prolonged HIV transmissibility. Using data from a nationally representative sample of 1,727 Black men in the 2015 Behavioral Risk Factor Surveillance System we tested for differences in poverty, depression, and health care barriers between Black men who had been HIV tested in the past year and those who had not. We also tested a syndemic index of these factors. Number of syndemic factors was linearly associated with less HIV testing (aPR=0.79, 95% CI 0.66-0.95). Assumptions of unidimensionality were met. The use of a syndemic index was a superior approach to analyzing these factors individually, both in terms of model fit and associations detected. The accumulation of poverty, depression, and health care barriers has an adverse impact on HIV testing among Black men.

Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization

This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.

Short-Term Truckload Spot Rates’ Prediction in Consideration of Temporal and Between-Route Correlations

Truckload spot rate (TSR), defined as a price offered on the spot to transport a certain cargo by using an entire truck on a target transportation line, usually price per kilometer-ton, is a key factor in shaping the freight market. In particular, the prediction of short-term TSR is of great importance to the daily operations of the trucking industry. However, existing predictive practices have been limited largely by the availability of multilateral information, such as detailed intraday TSR information. Fortunately, the emerging online freight exchange (OFEX) platforms provide unique opportunities to access and fuse more data for probing the trucking industry. As such, this paper aims to leverage the high-resolution trucking data from an OFEX platform to forecast short-term TSR. Specifically, a lagged coefficient weighted matrix-based multiple linear regression modeling (Lag-WMR) is proposed, and exogenous variables are selected by the light gradient boosting (LGB) method. This model simultaneously incorporates the dependency between historical and current TSR (temporal correlation) and correlations between the rates on alternative routes (between-route correlation). In addition, the effects of incorporating temporal and between-route correlations, time-lagged correlation and exogenous variable selection in modeling are emphasized and assessed through a case study on short-term TSR in Southwest China. The comparative results show that the proposed Lag-WMR model outperforms autoregressive integrated moving average (ARIMA) model and LGB in terms of model fitting and the quality and stability of predictions. Further research could focus on rates' standardization, to define a practical freight index for the trucking industry. Although our results are specific to the Chinese trucking market, the method of analysis serves as a general model for similar international studies.

Dynamic prediction systems of transition to psychosis using joint modelling: extensions to the base system

Seeking risk factors and constructing prediction models for transition to psychosis in individuals at ultra-high risk (UHR) has been an important research area. Our previous work showed that dynamic prediction could perform better than the conventional approach of using only baseline predictors in predicting transition to a psychotic disorder in UHR individuals. Dynamic prediction is the prediction of the occurrence of an event outcome using longitudinal data and has been made possible using a statistical methodology called joint modelling. The application of joint modelling and dynamic prediction in our previous work was relatively simple. In this paper, we examined extensions to our previous work in three ways: how to use the estimated changes in transition probability at repeated assessments over time to perform prediction, how to model the trajectory of the longitudinal data and how to model the relationship between the longitudinal data and the risk of transition to psychosis. Data from the Pace400 study (n = 398 UHR individuals), a follow-up study with transition to psychosis as the primary outcome, were used to investigate these extensions. Our results indicated that these extensions can enhance improvement in terms of model fit and sensitivity and specificity values. We have shown that dynamic prediction through joint modelling not only can utilize the richness of longitudinal data but also offers versatility in how prediction can be conducted. Our results have again confirmed that dynamic prediction via joint modelling should be considered as a useful tool for predicting transition to psychosis.

The latent structure of interpersonal problems: Validity of dimensional, categorical, and hybrid models.

Interpersonal problems are key transdiagnostic constructs in psychopathology. In the past, investigators have neglected the importance of operationalizing interpersonal problems according to their latent structure by using divergent representations of the construct: (a) computing scores for severity, agency, and communion (“dimensional approach”), (b) classifying persons into subgroups with respect to their interpersonal profile (“categorical approach”). This hinders cumulative research on interpersonal problems, because findings cannot be integrated both from a conceptual and a statistical point of view. We provide a comprehensive evaluation of interpersonal problems by enlisting several large samples (Ns = 5,400, 491, 656, and 712) to estimate a set of latent variable candidate models, covering the spectrum of purely dimensional (i.e., confirmatory factor analysis using Gaussian and nonnormal latent t-distributions), hybrid (i.e., semiparametric factor analysis), and purely categorical approaches (latent class analysis). Statistical models were compared with regard to their structural validity, as evaluated by model fit (corrected Akaike’s information criterion and the Bayesian information criterion), and their concurrent validity, as defined by the models’ ability to predict relevant external variables. Across samples, the fully dimensional model performed best in terms of model fit, prediction, robustness, and parsimony. We found scant evidence that categorical and hybrid models provide incremental value for understanding interpersonal problems. Our results indicate that the latent structure of interpersonal problems is best represented by continuous dimensions, especially when one allows for nonnormal latent distributions.

Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression

ABSTRACTThis study investigates the inclusion of spatio-temporal correlation and interaction in a multivariate random-parameters Tobit model and their influence on fitting areal crash rates with different severity outcomes. The spatial correlation is specified via a multivariate conditional autoregressiv (MCAR) prior, whereas the temporal correlation is specified by a linear time trend. A spatio-temporal interaction is formulated as the product of a time trend and a spatial term with an MCAR prior. A multivariate random-parameters spatio-temporal Tobit model is developed for slight injury and killed or serious injury crash rates using one year of crash data from 131 traffic analysis zones in Hong Kong. The proposed model is estimated and assessed in the Bayesian context. The model estimation results show that spatial and temporal effects and their interactive effects are significant and that the spatial and interactive effects have strong correlations across injury severities. The proposed model outperforms a multivariate random-parameters Tobit model and a multivariate random-parameters spatial Tobit model in terms of model fit. These findings highlight the importance of appropriately accommodating spatio-temporal correlation and interaction for the joint analysis of areal crash rates by severity.

Allometric biomass equations of Larix sibirica in the Altay Mountains, Northwest China

Boreal forests are important carbon sinks and have tremendous potential to mitigate climate change. Aboveground biomass of Siberian larch (Larix sibirica Ledeb.) stands in the Altay Mountains, Northwest China was studied and allometric equations that are related to the biomass of aboveground components using diameter at breast height (DBH) or both DBH and height (H) as independent variables for L. sibirica trees were derived in this paper. A linear simultaneous equation system by using either DBH or both DBH and H (DBH&H) indices, was used to ensure additivity of the biomass of individual tree components, and was fitted for L. sibirica. Model performance was validated using the jackknifing test. Results indicate that the goodness-of-fit for the regressions was lowest for the needles (R2 ranging from 0.696 to 0.756), and highest for the stem wood (R2 ranging from 0.984 to 0.997) and the aggregated biomass components (R2 ranging from 0.994 to 0.995). The coefficient of determination for each component was only marginally improved in terms of model fit and performance in the biomass equations that used DBH&H as the independent variables compared to that used DBH as the independent variable, and needles yielded an even worse fit. Stem biomass accounted for the largest proportion (87%) of the aboveground biomass. Based on the additive equations that used DBH as the single predicitor in this study, the mean aboveground carbon stock density and the carbon storage values of L. sibirica forests were 74.07 Mg C/hm2 and 30.69 Tg C, respectively, in the Altay Mountains. Empirical comparisons of published equations for the same species growing in the Altay Mountains of Mongolia were also presented. The mean aboveground carbon stock density estimated for L. sibirica forests was higher in the Chinese Altay Mountains than in the Mongolian Altay Mountains (66.00 Mg C/hm2).

Putting the pieces together: Recent growth, nutritional condition, and mortality of Engraulis anchoita larvae in the Southwest Atlantic

AbstractDynamics of clupeiform fish populations such as anchovy are frequently impacted by environmental variations which can affect the success of the species recruitment. Herein, we have analyzed recent otolith growth rate, RNA/DNA nutritional condition index (sRD), and mortality rate of argentine anchovy larvae Engraulis anchoita from three different nursery areas in the Southwest Atlantic. We have evaluated the relationship between the environmental variables (abundance of copepod nauplii, temperature, chlorophyll‐a concentration, and abundance of E. anchoita larvae) and larval endogenous variables (size, weight, age, and otolith radius) to sRD and recent growth rate. Fast larval growth rates were observed toward the northern sector of the studied area, characterized by higher temperature. High values of sRD were associated with higher nauplii abundance in the proximity of coastal fronts. The larvae with the lowest growths and lowest minimum values of nutritional condition coincided with the area where there was less abundance of nauplii and higher larval mortality. Larval size and nauplii abundance were positive explanatory factors for both recent growth rate and sRD index. Temperature had a positive effect on recent growth rate and a negative effect on sRD index. This condition index was poorly explained in terms of model fit in comparison with the growth model. The results herein provided could be significant to better understand the recruitment of the species, as to determining favorable areas for the growth and survival of anchovy larvae.

A shared spatial model for multivariate extreme-valued binary data with non-random missingness.

Clinical studies and trials on periodontal disease (PD) generate a large volume of data collected at various tooth locations of a subject. However, they present a number of statistical complexities. When our focus is on understanding the extent of extreme PD progression, standard analysis under a generalized linear mixed model framework with a symmetric (logit) link may be inappropriate, as the binary split (extreme disease versus not) maybe highly skewed. In addition, PD progression is often hypothesized to be spatially-referenced, i.e. proximal teeth may have a similar PD status than those that are distally located. Furthermore, a non-ignorable quantity of missing data is observed, and the missingness is non-random, as it informs the periodontal health status of the subject. In this paper, we address all the above concerns through a shared (spatial) latent factor model, where the latent factor jointly models the extreme binary responses via a generalized extreme value regression, and the non-randomly missing teeth via a probit regression. Our approach is Bayesian, and the inferential framework is powered by within-Gibbs Hamiltonian Monte Carlo techniques. Through simulation studies and application to a real dataset on PD, we demonstrate the potential advantages of our model in terms of model fit, and obtaining precise parameter estimates over alternatives that do not consider the aforementioned complexities.

Forecasting trade durations via ACD models with mixture distributions

Under Autoregressive Conditional Duration model framework, suitably modelling the positive valued innovations has been challenging. Most often, irregularly spaced trade duration data display a long right tail and a high density close to zero. Many subtle features and intricacies inherent in such time series demand flexible distributions to adequately capture these features. This paper introduces two mixture models by extending the mixture of exponential distributions of De Luca and Gallo [Mixture processes for financial intradaily durations. Stud. Nonlinear Dyn. Econ., 2004, 8(2), 1–20] to include more flexible and general distributions. In the initial extension, a Weibull distribution replaces one exponential component and, subsequently, a generalised beta of type 2 distribution is considered to further improve flexibility and extreme quantile estimation. Both these models are extended by incorporating dynamic mixture weights. Parameter estimation is done with a Bayesian methodology based on a Markov chain Monte Carlo sampling scheme. Simulation experiments are conducted to evaluate its performance and practical usefulness is assessed with empirical applications of trade durations from the Australian Securities Exchange. The performance of the proposed mixture models in comparison to several other competing distributions is evaluated in terms of model fit and forecast analysis via Time-at-Risk (TaR) quantiles and conditional expectation TaR (CTaR).

Item Response Models for Multiple Attempts With Incomplete Data

AbstractAllowance for multiple chances to answer constructed response questions is a prevalent feature in computer‐based homework and exams. We consider the use of item response theory in the estimation of item characteristics and student ability when multiple attempts are allowed but no explicit penalty is deducted for extra tries. This is common practice in online formative assessments, where the number of attempts is often unlimited. In these environments, some students may not always answer‐until‐correct, but may rather terminate a response process after one or more incorrect tries. We contrast the cases of graded and sequential item response models, both unidimensional models which do not explicitly account for factors other than ability. These approaches differ not only in terms of log‐odds assumptions but, importantly, in terms of handling incomplete data. We explore the consequences of model misspecification through a simulation study and with four online homework data sets. Our results suggest that model selection is insensitive for complete data, but quite sensitive to whether missing responses are regarded as informative (of inability) or not (e.g., missing at random). Under realistic conditions, a sequential model with similar parametric degrees of freedom to a graded model can account for more response patterns and outperforms the latter in terms of model fit.

Exploring influencing factors on transit ridership from a local perspective

Purpose Exploring the influencing factors on urban rail transit (URT) ridership is vital for travel demand estimation and urban resources planning. Among various existing ridership modeling methods, direct demand model with ordinary least square (OLS) multiple regression as a representative has considerable advantages over the traditional four-step model. Nevertheless, OLS multiple regression neglects spatial instability and spatial heterogeneity from the magnitude of the coefficients across the urban area. This paper aims to focus on modeling and analyzing the factors influencing metro ridership at the station level. Design/methodology/approach This paper constructs two novel direct demand models based on geographically weighted regression (GWR) for modeling influencing factors on metro ridership from a local perspective. One is GWR with globally implemented LASSO for feature selection, and the other one is geographically weighted LASSO (GWL) model, which is GWR with locally implemented LASSO for feature selection. Findings The results of real-world case study of Shenzhen Metro show that the two local models presented perform better than the traditional global model (OLS) in terms of estimation error of ridership and goodness-of-fit. Additionally, the GWL model results in a better fit than GWR with global LASSO model, indicating that the locally implemented LASSO is more effective for the accurate estimation of Shenzhen metro ridership than global LASSO does. Moreover, the information provided by both two local models regarding the spatial varied elasticities demonstrates the strong spatial interpretability of models and potentials in transport planning. Originality/value The main contributions are threefold: the approach is based on spatial models considering spatial autocorrelation of variables, which outperform the traditional global regression model – OLS – in terms of model fitting and spatial explanatory power. GWR with global feature selection using LASSO and GWL is compared through a real-world case study on Shenzhen Metro, that is, the difference between global feature selection and local feature selection is discussed. Network structures as a type of factors are quantified with the measurements in the field of complex network.

Measuring Social Sustainability with the Developed MCSA Model: Güzelyurt Case

The aim of the study is to develop a sample scale to measure social sustainability in an urban context. Social sustainability is an important component of sustainable development and it gained importance in the 1990s with awareness of the rising welfare of the society. The definitions, criteria, and the measurement system of social sustainability are still vague. In this context, this article bridges the gap in measuring social sustainability in an urban context and it proposes a model called Measurement of the City from Social Aspects (MCSA). The developed model was applied to the city of Güzelyurt, which has continuously experienced immigration from past to present. Quantitative research method was used in the research and the data was collected through interviews and a questionnaire survey, conducted with 400 local residents. The proposed model was tested in terms of model fit using confirmatory factor analysis. Also, the construct validity and discriminant validity of the model were tested. Research results indicate that the developed MCSA model works and it is suitable to be applied to other cities for measuring their social sustainability. Thus, the problems in the social aspects of the cities could be revealed and solutions could be generated to ensure the social sustainability of the cities.

Determinants of dividend payout and dividend propensity in an emerging market, Iran: an application of the LASSO

ABSTRACTAccurate prediction of dividends is important for market participants such as investors, firm managers, and monitoring authorities, as they can, respectively, invest, manage dividend decisions, and monitor dividend policies more effectively. We identify the most relevant variables for predicting the dividend payout of the firms in an emerging market, Iran, using the least absolute shrinkage and selection operator (LASSO). The advantages of the LASSO include: enhancing the prediction accuracy of the dividend model, improving interpretation of the results, and applicability to high-dimensional data. We obtain several results. First, some fundamental determinants of dividends in the industrialized economies such as market-to-book ratio and current ratio, do not play a role in deciding dividends in Iran. Second, LASSO-selected variables outperform the variables commonly used in the literature in terms of model fit and prediction accuracy. Third, business risk, leverage, return on assets and effective tax rate are the most important predictors of dividend propensity of the Iranian firms. Fourth, if the support vector machine algorithm, an often-used classification method, is combined with LASSO-selected variables, it can better discriminate between dividend-paying and dividend non-paying firms than other methods such as logistic regression and linear discriminant analysis.Abbreviations: LASSO: Least Absolute Shrinkage and Selection Operator; TSE: Tehran Stock Exchange; RMSE: Root Mean Squared Errors; MAE: Mean Absolute Errors; ROC: Receiver Operating Characteristics; GMM: Generalized Method of Moments; MENA: Middle East and North Africa region; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; LARS: Least Angel Regression; OLS: Ordinary Least Squares; AUC: Area Under Curve; BS: Brier Score ; OA: Overall Accuracy; LDA: Linear Discriminant Analysis; SVM: Support Vector Machine algorithm; LR: Logistic Regression.

Modeling discretionary lane-changing behavior on urban streets considering drivers’ heterogeneity

ABSTRACTLane changes are particularly frequent on urban streets, which not only impact traffic operations, but also cause negative effects on safety. This study uses vehicle trajectory data collected from Southwest Road in Dalian, China, to investigate the influential factors of discretionary lane changing in an urban road environment. Both the standard logit model and mixed logit model were fitted to the data, which evidently identified several key factors. The mixed logit model outperformed the standard logit model in terms of model fit. The results suggest that driver heterogeneity is present on the speed differentials of the subject vehicle and the leading vehicles, and the distance gap in the target lane. A sensitivity analysis was further conducted to quantify the degree of influence of the statistically significant variables. The findings support the notion and purpose of discretionary lane changing, i.e. seeking a speed advantage and/or a more satisfactory driving environment.