Terms Of Model Fit Research Articles

Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning.

Assessment of Pain-related Disability in Pediatric Chronic Pain: A Comparison of the Functional Disability Inventory and the Pediatric Pain Disability Index.

The assessment of pain-related disability in pediatric chronic pain patients plays an important role in research on the effectiveness of pain treatment. The present study aimed to compare the 2 available measures of pain-related disability in the German language, the Functional Disability Inventory (FDI) and Pediatric Pain Disability Index (PPDI), in terms of psychometric properties and convergent validity. In addition, associations between FDI/PPDI scores and sex, age, and pain locations were investigated. Data of N=250 pediatric pain patients (Mage=14.4, SD=1.8) were collected at the patients' initial presentation to a specialized pediatric pain center. The results revealed similarly sound psychometric properties in terms of model fit, item difficulties, corrected item-total correlations, and missing values for both measures. However, item difficulties for the FDI were rather low. Both measures displayed good convergent validity in terms of associations with pain intensity, pain-related school absence, and emotional functioning (anxiety, depression). In both measures, girls had significantly higher scores than boys. No associations were found between the FDI/PPDI total scores and age or pain location. In conclusion, both measures are equally suited to assess pain-related disability in adolescents with chronic pain. The FDI should be used with caution in epidemiological studies due to the risk of floor effects. Further research is needed with regard to the relationship between pain-related disability and sex.

Transferability and Scalability of Soil Total Carbon Prediction Models in Florida, USA

The applicability, transferability, and scalability of visible/near-infrared (VNIR)-derived soil total carbon (TC) models are still poorly understood. The objectives of this study were to: i) compare models of three multivariate statistical methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest methods, to predict soil logarithm-transformed TC (logTC) using five fields (local scale) and a pooled (regional-scale) VNIR spectral dataset (a total of 560 TC spectral datasets), ii) assess the model transferability among fields, and iii) evaluate their up- and downscaling behaviors in Florida, USA. The transferability and up- and downscaling of the models were limited by the following factors: i) the spectral data domain, ii) soil attribute domain, iii) methods that describe the internal model structure of VNIR-TC relationships, and iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R2 > 0.86, bias < 0.01%, root mean squared error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70%, and ratio of prediction error to interquartile range (RPIQ) > 4.54. The PLSR method performed substantially better than the SVM method to scale and transfer the TC models. This could be attributed to the tendency of SVM to overfit models, while the asset of the PLSR method was its robustness when the models were validated with independent datasets, transferred, and/or scaled. The upscaled soil TC models performed somewhat better in terms of model fit (R2), RPD, and RPIQ, whereas the downscaled models showed less bias and smaller RMSE based on PLSR. We found no universal trend indicating which of the four limiting factors mentioned above had the most impact that constrained the transferability and scalability of the models. Given that several factors can impinge on the empirically derived soil spectral prediction models, as demonstrated by this study, more focus on their applicability and scalability is needed.

Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.

Cognitive diagnostic model of best choice: a study of reading comprehension

The present study compared the performance of six cognitive diagnostic models (CDMs) to explore inter skill relationship in a reading comprehension test. To this end, item responses of about 21,642 test-takers to a high-stakes reading comprehension test were analyzed. The models were compared in terms of model fit at both test and item levels, classification consistency and accuracy, and proportion of skill mastery profiles. The results showed that the G-DINA performed the best and the C-RUM, NC-RUM, and ACDM showed the closest affinity to the G-DINA. In terms of some criteria, the DINA showed comparable performance to the G-DINA. The test-level results were corroborated by the item-level model comparison, where DINA, DINO, and ACDM variously fit some of the items. The results of the study suggested that relationships among the subskills of reading comprehension might be a combination of compensatory and non-compensatory. Therefore, it is suggested that the choice of the CDM be carried out at item level rather than test level.

Scaling properties of pain intensity ratings in paediatric populations using the Faces Pain Scale-revised: Secondary analyses of published data based on the item response theory

BackgroundThe Faces Pain Scale-revised (FPS-r) has been developed as an interval scale. For other pain measurement instruments, several studies found evidence for and against an interval level of measurement. ObjectivesThe primary aim of the current study was to evaluate the scale properties of the FPS-r using an item response theory approach. DesignSecondary analysis of published data. SettingThree studies; Study 1 and study 2: One university hospital; Study 3: international pain registry. ParticipantsStudy 1: n = 246, female: 41%, age: 11–18 years, 3 pain items; Study 2: n = 240, female: 43%, age: 11–18 years, 9 pain items; Study 3: n = 2266, female: 41%, age: 4–18 years, 3 pain items. MethodsThe rating scale model (interval scale), the graded response model (no interval scale, ordered response categories) and the partial credit model (no interval scale) were used to scale the data. ResultsIn all three studies, the rating scale model was outperformed by the graded response model or the partial credit model in terms of model fit. Overlapping response categories were found in items associated with less pain. Response category widths were wider for categories associated with low pain intensity and smaller for categories associated with high pain intensities. Smallest response categories were 1%–67% smaller compared to the widest response category of the same item. ConclusionAccording to these findings, the interval scale properties of the FPS-r may be questioned. Item response theory methods may help to solve the problem of missing linearity in pain intensity ratings using FPS-r.

Hierarchical Bayesian Analyses for Modeling BOLD Time Series Data

Hierarchical Bayesian analyses have become a popular technique for analyzing complex interactions of important experimental variables. One application where these analyses have great potential is in analyzing neural data. However, estimating parameters for these models can be complicated. Although many software programs facilitate the estimation of parameters within hierarchical Bayesian models, due to some restrictions, complicated workarounds are sometimes necessary to implement a model within the software. One such restriction is convolution, a technique often used in neuroimaging analyses to relate experimental variables to models describing neural activation. Here, we show how to perform convolution within the R programming environment. The strategy here is to pass the convolved neural signal to existing software package for fitting hierarchical Bayesian models to data such as JAGS (Plummer 2003) or Stan (Carpenter et al. 2017). We use the convolution technique as a basis for describing neural time series data and develop five models to describe how subject-, condition-, and brain-area-specific effects interact. To provide a concrete example, we apply these models to fMRI data from a stop-signal task. The models are assessed in terms of model fit, parameter constraint, and generalizability. For these data, our results suggest that while subject and condition constraints are important for both fit and generalization, region of interest constraints did not substantially improve performance.

A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade

Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each other due to spatial and network autocorrelation, these methods may lead to biased parameter estimates. To overcome this problem, eigenvector spatial filtering (ESF) variants of the Poisson/negative binomial specifications have been proposed in the literature on gravity modelling of trade. However, no specific treatment has been developed for cases in which many zero flows are present. This paper contributes to the literature in two ways. First, by employing a stepwise selection criterion for spatial filters that is based on robust (sandwich) p-values and does not require likelihood-based indicators. In this respect, we develop an ad hoc backward stepwise function in R. Second, using this function, we select a reduced set of spatial filters that properly accounts for importer-side and exporter-side specific spatial effects, as well as network effects, both at the count and the logit processes of zero-inflated methods. Applying this estimation strategy to a cross-section of bilateral trade flows between a set of 64 countries for the year 2000, we find that our specification outperforms the benchmark models in terms of model fitting, both considering the AIC and in predicting zero (and small) flows.

On individual-specific prediction of hidden inbreeding depression load.

Inbreeding depression is caused by increased homozygosity in the genome and merges two genetic mechanisms, a higher impact from recessive mutations and the waste of overdominance contributions. It is of major concern for the conservation of endangered populations of plants and animals, as major abnormalities are more frequent in inbred families than in outcrosses. Nevertheless, we lack appropriate analytical methods to estimate the hidden inbreeding depression load (IDL) in the genome of each individual. Here, a new mixed linear model approach has been developed to account for the inbreeding depression-related background of each individual in the pedigree. Within this context, inbred descendants contributed relevant information to predict the IDL contained in the genome of a given ancestor; moreover, known relationships spread these predictions to the remaining individuals in the pedigree, even if not contributing inbred offspring. Results obtained from the analysis of weaning weight in the MARET rabbit population demonstrated that the genetic background of inbreeding depression distributed heterogeneously across individuals and inherited generation by generation. Moreover, this approach was clearly preferred in terms of model fit and complexity when compared with classical approaches to inbreeding depression. This methodology must be viewed as a new tool for a better understanding of inbreeding in domestic and wild populations.

Estimating the Marginal Implicit Price of the Spatially Diverse Environmental Amenities from Urban Open Space in Onondaga County, New York: A Geographically Weighted Regression Approach

This research applies Geographically Weighted Regression (GWR) to investigate how different types of urban open space and their spatial configurations are valued by residential property owners. To reflect residential property owners’ perception of neighborhood and spatially heterogeneous configuration of urban open spaces, environmental amenity variables are measured at 100m and 1km radius buffer surrounding each residential property. GWR and Ordinary Least Squares (OLS) models are applied to analyze 4469 arms-length residential property transactions from Onondaga County, NY (USA) between Jan. 2000 and Dec. 2000 and are evaluated in terms of model fitting and spatial effects assessment. Results of this research show that GWR models outperform OLS models in model fitting and detecting spatial effects in model residuals, as well as is able to find spatial non-stationary relationships in larger number of environmental amenity variables. This research innovates in several ways. First, it innovates by considering spatial configuration of open spaces reflecting residential property owners’ perspective. Second, this research illustrated spatially varying effect of urban open space variables using the marginal implicit price of each variable.

Exploring risk factors in breast cancer screening program data using structured geoadditive models with high order interaction

When analyzing data from cancer screening programs, flexible regression specifications are required to account for the highly complex structure in such data. We analyzed data from a breast cancer screening program conducted in central Portugal and considered an extension of structured additive regression models where, in addition to the possibility to include nonlinear and spatial effects, we can include a trivariate interaction between attendance rate, detection rate and mortality rate in the screening program. While spatial effects capture unobserved heterogeneity at the municipality level, the trivariate interaction proves important for the understanding of the complex interaction effects resulting from the diversity in municipality coverage and attendance rates. The trivariate interaction is implemented based on a Markov random field representation which enables efficient Bayesian inference and, when modeling breast cancer incidence rates, showed a significant improvement in terms of model fit when compared to a simpler geoadditive regression model.

Park-and-ride lot choice model using random utility maximization and random regret minimization

This research aims to understand the park-and-ride (PNR) lot choice behaviour of users i.e., why PNR user choose one PNR lot versus another. Multinomial logit models are developed, the first based on the random utility maximization (RUM) concept where users are assumed to choose alternatives that have maximum utility, and the second based on the random regret minimization (RRM) concept where users are assumed to make decisions such that they minimize the regret in comparison to other foregone alternatives. A PNR trip is completed in two networks, the auto network and the transit network. The travel time of users for both the auto network and the transit network are used to create variables in the model. For the auto network, travel time is obtained using information from the strategic transport network using EMME/4 software, whereas travel time for the transit network is calculated using Google’s general transit feed specification data using a backward time-dependent shortest path algorithm. The involvement of two different networks in a PNR trip causes a trade-off relation within the PNR lot choice mechanism, and it is anticipated that an RRM model that captures this compromise effect may outperform typical RUM models. We use two forms of RRM models; the classical RRM and µRRM. Our results not only confirm a decade-old understanding that the RRM model may be an alternative concept to model transport choices, but also strengthen this understanding by exploring differences between two models in terms of model fit and out-of-sample predictive abilities. Further, our work is one of the few that estimates an RRM model on revealed preference data.

Optimization of Operating Conditions for the Production of Enhanced Antifungal Metabolites from Streptomonospora arabica VSM 25 by Full Factorial Design

: Objectives: To execute the influence of the physico-chemical variables on the production of the bioactive metabolites by Streptomonospra arabica VSM-25 using Response Surface Methodology. Materials and Methods: An actinobacterium strain isolated from the deep sea marine sediment samples was identified as Streptomonospra arabica VSM-25 by conventional and molecular approaches. RSM was employed to study the impact of five variables, viz. incubation time, pH, temperature, galactose and peptone concentrations on the production of antifungal metabolites by VSM-25. Growth related production formation kinetics and substrate utilization in batch system was analysed using mathematical and unstructured kinetic models. Results: Statistical study showed that the incubation time, pH, Temperature, Concentration of galactose and peptone has a significant effect (p <0.0001) on the bioactive metabolite production at their individual and interactive level. A second order polynomial model provided a satisfied fit for experimental data with regard to the production of antifungal metabolites. Maximum antimycotic activity was achieved at incubation time (11 days), pH (8), temperature (30°C), galactose concentration (2%) and peptone concentration (1%). Unstructured mathematical kinetic model was developed and estimated kinetic parameters also exhibited good approximation in terms of model fitting and regression analysis. Conclusion: To the best of our knowledge this is the first report on the production of anti fungal metabolites from S. arabica using RSM and kinetic modelling studies which firmly support the application of RSM and kinetic modelling for optimization. The study may find potential application in rapid screening and production of novel drug molecules from unexploited natural sources.Key words: Streptomonospora arabica, Antimycotic Activity, Optimization, Kinetic Modelling, Response Surface Methodology, Bioactive Metabolites.

Run-to-Run Optimization of Batch Processes Using Set-Based Constraints

Discrepancies between model and process responses typically result from modelling simplifications and assumptions. In the presence of structural model-plant mismatch, a run-to-run optimization procedure that follows a standard repetitive “two-step” approach, involving identification followed by optimization, may not converge to the process optimum. Parameter adaptation is a methodology that can be used to correct for model error at each batch run to drive the process to an optimum. Model parameters are updated during each iteration (batch) to account for changes in operating points as well as to correct for errors in the predicted gradients of the cost function and constraints. However, parameter values which results in a good model fit in terms of fitting the predicted outputs to data do not necessarily give an accurate prediction of the gradients of the cost function, which are necessary for reaching the optimum. We propose a parameter estimation objective which not only minimizes the differences between measurements and predictions but also maximizes the sensitivities of the cost function and constraint gradients with respect to the parameters. Towards these goals, we make use of set-based constraints which enforce uncertainty bounds over the model predictions. The resulting improvements in convergence to the optimum as compared to earlier studies are illustrated using a simulated case study of a penicillin fed-batch process.

Assessing two consumer behavioural intention models in a service environment

PurposeThe purpose of this paper is to examine two alternative consumer behavioural intention models that have been developed from the marketing and information systems disciplines in a service environment. Specifically, it reports an empirical assessment of the two non-nested structural models in the context of Australian restaurant industry.Design/methodology/approachThis study used a web-based survey by an online research organization and structural equation modelling with AMOS was used to compare the two non-nested behavioural intention models.FindingsThis study found that the second model that incorporates expectation-confirmation theory outperformed the first model in terms of model fit with the empirical data.Practical implicationsThe findings of this study provide service managers with important insights into the appropriate design of service delivery systems to increase consumer satisfaction which, in turn, leads to more positive behavioural intentions. Moreover, the restaurant research setting means that marketing managers in the growing tourism and hospitality industry should benefit from the study findings.Originality/valueThis study synthesized two consumer behavioural intention models from different disciplines and provided an approach to the empirical comparison of the non-nested structural models.

Smooth Transition Spatial Autoregressive Models

This paper introduces a new model for spatial time series in which cross-sectional dependence varies nonlinearly over space by means of smooth transitions. We refer to our model as the Smooth Transition Spatial Autoregressive (ST-SAR). We establish consistency and asymptotic Gaussianity for the MLE under misspecification and provide additional conditions for geometric ergodicity of the model. Simulation results justify the use of limit theory in empirically relevant settings. The model is applied to study spatio-temporal dynamics in two cases that differ in spatial and temporal extent. We study clustering in urban densities in a large number of neighborhoods in the Netherlands over a 10-year period. We pay particular focus to the advantages of the ST-SAR as an alternative to linear spatial models. In our second study, we apply the ST-SAR to monthly long term interest rates of 15 European sovereigns over 25-year period. We develop a strategy to assess financial stability across the Eurozone based on attraction of individual sovereigns toward the common stochastic trend. Our estimates reveal that stability attained a low during the Greek sovereign debt crisis, and that the Eurozone has remained to struggle in attaining stability since the onset of the financial crisis. The results suggest that the European Monetary System has not fully succeeded in aligning the economies of Ireland, Portugal, Italy, Spain, and Greece with the rest of the Eurozone, while attraction between other sovereigns has continued to increase. In our applications linearity of spatial dependence is overwhelmingly rejected in terms of model fit and forecast accuracy, estimates of control variables improve, and residual correlation is better neutralized.

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.

Methodological issues in measuring subjective well-being and quality-of-life: Applications to assessment of affect in older, chronically and cognitively impaired, ethnically diverse groups using the Feeling Tone Questionnaire.

Quality of life assessment includes measurement of positive affect. Methods artifacts associated with positively and negatively worded items can manifest as negative items loading on a second factor, despite the conceptual view that the items are measuring one underlying latent construct. Negatively worded items may elicit biased responses. Additionally, item-level response bias across ethnically diverse groups may compromise group comparisons. The aim was to illustrate methodological approaches to examining method factors and measurement equivalence in an affect measure with 9 positively and 7 negatively worded items: The Feeling Tone Questionnaire (FTQ). The sample included 4,960 non-Hispanic White, 1,144 non-Hispanic Black, and 517 Hispanic community and institutional residents receiving long-term supportive services. The mean age was 82 (s.d.=11.0); 73% were female. Two thirds were cognitively impaired. Methods effects were assessed using confirmatory factor analyses (CFA), and reliability with McDonald's omega and item response theory (IRT) generated estimates. Measurement equivalence was examined using IRT-based Wald tests. Methods effects associated with negatively worded items were observed; these provided little IRT information, and as a composite evidenced lower reliability. Both 13 and 9 item positive affect scales performed well in terms of model fit, reliability, IRT information, and evidenced little differential item functioning of high magnitude or impact. Both CFA and IRT approaches provided complementary methodological information about scale performance. The 9-item affect scale based on the FTQ can be recommended as a brief quality-of-life measure among frail and cognitively impaired individuals in palliative and long-term care settings.

Semiparametric Bayesian Regression via Potts Model

ABSTRACTWe consider Bayesian nonparametric regression through random partition models. Our approach involves the construction of a covariate-dependent prior distribution on partitions of individuals. Our goal is to use covariate information to improve predictive inference. To do so, we propose a prior on partitions based on the Potts clustering model associated with the observed covariates. This drives by covariate proximity both the formation of clusters, and the prior predictive distribution. The resulting prior model is flexible enough to support many different types of likelihood models. We focus the discussion on nonparametric regression. Implementation details are discussed for the specific case of multivariate multiple linear regression. The proposed model performs well in terms of model fitting and prediction when compared to other alternative nonparametric regression approaches. We illustrate the methodology with an application to the health status of nations at the turn of the 21st century. Supplementary materials are available online.

Predicting adoption of biological control among Iranian rice farmers: An application of the extended technology acceptance model (TAM2)

Biological control (BC) strategies have bolstered the ability and motivation of farmers to control various types of pests, but better understanding of farmers' behaviour for BC acceptance is still needed. This study attempted for the first time to implement an extended version of the technology acceptance model (TAM) that combined innovation diffusion theory (IDT), perceived self-efficacy, and facilitating conditions to investigate factors affecting farmers' acceptance and use of BC for the control of rice stem borer [Chilo suppressalis (Walker)] in rice fields of Mazandaran province in northern Iran. The proposed model (widely known as TAM2) was empirically tested using data collected from a survey of 179 rice farmers. The TAM2 explained 78% of the variance in behavioural intention of farmers to use BC and 82% of the variance in actual use. With the TAM2, adoption and use of BC could be adequately predicted from farmers' intentions, which were affected by perceived self-efficacy, facilitating conditions, compatibility, and perceived usefulness as the original TAM suggests. Therefore, with the TAM2 we successfully extended the TAM (in terms of model fit) by including three external factors relevant to BC, namely, perceived self-efficacy, facilitating conditions, and compatibility as antecedents of behavioural intention to use. However, the predicted direct and indirect effects of perceived ease of use on farmers' behavioural intention were not supported. The TAM2 outperformed the original TAM in predicting farmer's intention to use BC in farm practices and consequently actual use. To increase acceptance of BC, agricultural authorities should take care of creating a favorable environment which will support and encourage the use of BC at the farm level. Providing facilitating conditions, such as extension services and training, can help farmers to increase their knowledge about BC. However, more emphasis should be given on compatibility and usefulness of this technology and less on the ease of use.