Significant Improvement In Model Fit Research Articles

Flexible Modeling of Nonstationary Extremal Dependence using Spatially Fused LASSO and Ridge Penalties

Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Parametric max-stable processes (MSPs) are common choices for modeling spatially indexed block maxima, where an assumption of stationarity is usual to make inference feasible. However, this assumption is unrealistic for data observed over a large or complex domain. We develop a computationally efficient method for estimating extremal dependence using a globally nonstationary but locally stationary MSP construction, with the spatial domain divided into a fine grid of subregions, each with its own dependence parameters. We use LASSO ( L 1 ) or ridge ( L 2 ) penalties to obtain spatially smooth parameter estimates. We then develop a novel data-driven algorithm to merge homogeneous neighboring subregions. The algorithm facilitates model parsimony and interpretability. To make our model suitable for high-dimensional data, we exploit a pairwise likelihood to perform inference and discuss its computational and statistical efficiency. We apply our proposed method to model monthly maximum temperature data at over 1400 sites in Nepal and the surrounding Himalayan and sub-Himalayan regions; we show significant improvements in model fit compared to a stationary model. Furthermore, we demonstrate that the estimated merged partition is interpretable from a geographic perspective and leads to better model diagnostics by adequately reducing the number of parameters.

Identifying the Careless Responders: A Cross-Country Comparison on PISA 2018 Dataset

Careless responding (CR) is one of the major concerns in self-report measures since it affects the quality of collected data. In this research, it was aimed to examine the dataset in terms of CR, to investigate the effects of CR on the psychometric properties of data, and to examine the effectiveness of CR detection methods in a cross-cultural context. Specifically; response time, outlier and long-string indices were used to flag CR and efficiency of using these were compared on PISA 2018 reading related attitude scales for Singapore and Turkey. As a result, it was indicated that the amount of CR was higher for Singapore than Turkey at long-string and response time methods. Furthermore, after removal of CR from dataset, enjoyment level of reading, perception of competence and perception of difficulty of PISA tests scores increased. Another result was that long-string and response time analyses provided significant improvement in model fit and long-string had the highest improvement level. Moreover, removing respondents according to outlier analyses didn’t provide any improvement on model-data fit. In general, results illustrated that careless responding behavior may have important effects on psychometric properties of self-report and screening the data for CR is strongly suggested in future studies.

Management’s tone change in MD&A and tax avoidance

Purpose The purpose of this paper is to explore how variations in management’s tone within management’s discussion and analysis (MD&A) sections of 10-K reports can serve as an indicator of tax avoidance and highlight the complex relationship between such linguistic shifts and the tax avoidance decisions within firms. Design/methodology/approach The paper uses a textual analysis approach to identify linguistic cues in MD&A sections of 10-K filings related to tax avoidance, going beyond traditional quantitative measures. The study uses differences in negative word occurrences in MD&A to measure management’s tone change and examines various measures of tax avoidance. The sample covers the period from 1993 to 2017 and comprises all firms with 10-K filings available on EDGAR, totaling over 30,000 firm-year observations. Findings The findings indicate a complementary relationship between tax avoidance and other drivers of firm performance. When firms have more negative management’s tone, they are less willing to engage in tax avoidance and vice versa. The study’s approach with management’s tone change provides a different and statistically significant improvement in model fit for detecting tax avoidance. Practical implications This paper provides actionable insights for detecting tax avoidance through the analysis of management’s tone in corporate disclosures, offering a new tool for researchers, investors and tax authorities. It highlights the importance of linguistic cues as indicators of tax avoidance behavior, complementing traditional financial metrics. Originality/value The paper contributes to the literature by using management’s tone change as a time-varying factor to explain tax avoidance behavior. It uncovers a larger set of linguistic cues in MD&A that can be used to detect tax avoidance. This research provides a complementary approach to traditional quantitative tax avoidance measures and offers insights into the overall relationship between tax avoidance and firm performance, going beyond one-dimensional measures typically used in prior literature.

Partial proportional odds model for analyzing pedestrian crashes, threshold heterogeneity by scale and proportional odds factor

Despite low traffic in Wyoming, pedestrian crash severity accounts for a high number of fatalities in the state. Thus this study was conducted to highlights factors contributing to those crashes. The results highlighted that drivers under influence, type of vehicle, location of crashes, estimated speed of vehicles, driving over the recommended speed are some of factors contributing to the severity of crashes. In this study, we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values. However, it has been argued that this assumption might be unrealistic, especially at the presence of extreme values. Thus, the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes, or proportional odds effects. In addition, we accounted for the spread rate, or scale, of the model's latent distribution of pedestrian crashes. The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model. Comparisons were also made across standard normal, simple partial ordinal model, and partial ordinal accounting for scale heterogeneity. In addition, various potential threshold structures such as symmetric and flexible were considered, but similar goodness of fits were observed across all those models. Extensive discussion has been made regarding the formulation of the implemented methodology, and its implications. • More flexibility is given to the model by relaxing the assumption of proportional odds and scale, or spread rate. • The results highlight a significant improvement in the model fit, compared with the standard model. • Some of factors contributing to the severity of pedestrian crashes are discussed. • Almost all identified results linked to the lack of attention and caution of the drivers.

Process representation of conifer tree-ring growth is improved by incorporation of climate memory effects

Process modeling of tree growth responses to climate can improve our understanding of the functioning of tree growth under climate change, but these models do not consider so-called memory effects. Further, memory effects are unstudied in temperate forests of China, which is showing high vulnerability to climate warming. Here, we integrate a process-based Vaganov–Shashkin–Lite (VS-Lite) model of tree ring formation with the stochastic antecedent modeling (SAM) approach, to quantify the current and antecedent climatic drivers of the tree-ring width chronologies of 109 conifers in temperate China. Based on climate-response patterns, tree-ring width series were clustered into “warm-dry,” “cold-dry,” and “cold-wet.” The VS-Lite results indicated growth in the “warm-dry” and “cold-dry” sites were primarily constrained by current soil moisture, while growth in the “cold-wet” sites was mainly constrained by cotemporaneous temperature. Compared to VS-Lite alone, we found significant improvements in model fit for the SAM framework in all forest types. Notably, antecedent drivers explained 14%, 11%, and 21% of the growth variations in “warm-dry,” “cold-dry,” and “cold-wet” sites, respectively, challenging the assertion that long memory is only important in dry forests. Commonality analysis revealed that growth in “cold-dry” and “cold-wet” sites was determined by antecedent climate while growth in the “cold-dry” and “warm-dry” clusters were determined by the endogenous (autoregressive) memory. In “warm-dry” and “cold-wet” sites, growth was significantly affected by antecedent temperature in opposite directions, whereas in the “cold-dry” sites it was affected by antecedent soil moisture conditions and soil moisture-temperature interactions. Our combined process-empirical model demonstrates that memory is prevalent across temperate forests in China and also improves representation of tree growth in diverse forests under novel climate conditions of the future.

Development of a nomogram for predicting lymph node metastasis in patients with urothelial carcinoma of the bladder

Background and Objectives: Multiple nomograms have previously been described to predict recurrence, overall, and cancer-specific survival following radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB).Our aim was to develop a nomogram for the preoperative prediction of lymph node (LN) metastasis in UCB. Methods: We prospectively collected data from 483 patients which were used in construction of the statistical model. The variables considered as predictors in the model were demographic, histopathological and radiological factors. Results: The full model containing all 12 covariates produced an AIC (Akaike information criterion) value of 448.9. After model selection T-stage, grade, CIS (carcinoma in situ), pathology, LV (lymphovascular) invasion and CT (computed tomography) were included as the most parsimonious model while retaining predictive accuracy. Ta in 82 (17%), T1 in 214 (445) and T2 in 187 (38%) patients. This model had an AIC of 436.4, indicating a significant improvement in model fit after the removal of unimportant predictors. The C-indices were 0.821 and 0.808 for the reduced model and the full model, respectively, indicating greater discrimination ability for the reduced model.The nomogram further emphasises the effect of CT and LV invasion on the risk of LN positivity. Specifically, regardless of all other variables, a patient with a CT will have 100 points more than a patient without a CT, corresponding to a difference in risk of approximately 40%. The odds of LN positivity for patients with a CT are 7.45 times that of patients without a CT, regardless of all other covariates. LV invasion, pathology, CIS and T-stage are also statistically significant ( p = 0.05). Conclusion: This nomogram is a preoperative prediction tool that uses different preoperative variables with acceptable predictive accuracy for LN metastasis in patients with BC.

Statistical comparison of DEC and DEC+J is identical to comparison of two ClaSSE submodels, and is therefore valid

AbstractAimStatistical model comparison has become common in historical biogeography, enabled by the R package BioGeoBEARS, which implements several models in a common framework, allowing models to be compared with standard likelihood‐based methods of statistical model comparison. Ree and Sanmartín (Journal of Biogeography, 45, 741–749, 2018) critiqued the comparison of Dispersal–Extinction–Cladogenesis (DEC) and a modification of it, DEC+J, which adds the process of jump dispersal at speciation. DEC+J provides highly significant improvements in model fit on most (although not all) datasets. They claim that the comparison is statistically invalid for a variety of reasons. I analyse the key claims made by the critique.LocationSimulated data.TaxonSimulated data.MethodsLikelihood calculations are checked by comparison between programs and by‐hand calculations, and by summing likelihoods across all possible datasets. Model adequacy of DEC versus DEC+J is checked by a simulation/inference experiment.ResultsMistakes in the critique's example likelihood calculations are demonstrated. DEC+J fits better on datasets because the DEC model is statistically inadequate in the common situation when most species have geographical ranges of single areas; the DEC model requires long residence times of multi‐area ranges, and when these are not observed, a model that does produce such data patterns, such as DEC+J, prevails. More fundamentally, statistical comparison of DEC and DEC+J produces identical log‐likelihood differences to statistical comparison of two submodels of ClaSSE where extinction rates are fixed to 0.Main ConclusionsDEC fails a basic model adequacy check for understandable reasons, while DEC+J does not. As Ree and Sanmartín recommend ClaSSE models as valid for comparison, the comparison of DEC and DEC+J is statistically valid according to their own criteria.

Prospective assessment of sparing the parotid ducts via MRI sialography for reducing patient reported xerostomia

PurposeTo assess the impact of prospectively sparing the parotid ducts via MRI sialography on patient reported xerostomia for those receiving definitive radiotherapy (RT) for oropharyngeal squamous cell carcinoma. Methods and materialsThirty-eight patients with oropharynx cancer to be treated with definitive RT underwent pre-treatment MRI sialograms to localize their parotid ducts. The parotid ducts were maximally spared during treatment planning. Patients reported symptoms (PRO-CTCAE and QLQ-H&N35) were collected at 6 and 12 months post-RT and compared to a historical cohort who underwent conventional parotid gland mean dose sparing. Regression models were generated using parotid and submandibular gland doses with and without incorporating the dose to the parotid ducts to determine the impact of parotid duct dose on patient reported xerostomia. ResultsAt 6 months post-RT, 12/26 (46%) patients reported ≥moderate xerostomia when undergoing parotid ductal sparing compared to 43/61 (70%) in the historical cohort (p = 0.03). At 12 months post-RT, 8/22 (36%) patients reported ≥moderate xerostomia when undergoing parotid ductal sparing compared to 34/68(50%) in the historical cohort (p = 0.08). Using nested logistic regression models, the mean parotid duct dose was found to significantly relate to patient reported xerostomia severity at 6 months post-RT (p = 0.04) and trended towards statistical significance at 12 months post-RT (p = 0.09). At both 6 and 12 months post-RT, the addition of mean parotid duct dose significantly improved model fit (p < 0.05). ConclusionsMRI sialography guided parotid duct sparing appears to reduce the rates of patient-reported xerostomia. Further, logistic regression analysis found parotid duct dose to be significantly associated with patient reported xerostomia. A significant improvement in model fit was observed when adding mean parotid duct dose compared to models that only contain mean parotid gland dose and mean contralateral submandibular gland dose.

Kinetic Modeling of Ex Vivo Lung Perfusion Biomarkers for the Prediction of Lung Transplant Outcomes

<h3>Purpose</h3> Many ex vivo lung perfusion (EVLP) biomarker studies involve hourly perfusate sampling to predict lung transplant outcomes; however, the diagnostic and predictive value of repeated sampling and a biomarker kinetic profile remains unknown. In this study, we constructed mathematical models of protein biomarkers in EVLP perfusate and assessed the predictive value of the kinetic model features. <h3>Methods</h3> Clinical EVLP cases (n=45) were used in this study. Perfusate samples were collected every 15 minutes from 0-180 minutes of perfusion. Seven protein biomarkers were studied: GM-CSF, IL-10, IL-1β, IL-6, IL-8, sTNFR1, and sTREM1. All concentration data were corrected for circuit dilution using the reported standardized perfusate exchanges. Five mathematical models were tested to fit the biomarker time-series data: linear, quadratic, exponential, four- and five-parametric logistic regression. The model features were then used to predict recipient ICU length of stay using the area under the receiver operating characteristic curve (AUROC). <h3>Results</h3> The time-series data showed that GM-CSF (r²=0.92), sTNFR1 (r²=0.95), sTREM1 (r²=0.96) were best described using a linear model, whereas, IL-10 (r²=0.93), IL-1β (r²=0.92), IL-6 (r²=0.95), and IL-8 (r²=0.98) were best described by an exponential growth curve. When biomarker data were corrected for dilution, there was a significant improvement in model fit [p<0.05 (IL-8, GM-CSF); p<0.001 (IL-1β, IL-10, IL-6, sTNFR1, sTREM1)]. For linear models of GM-CSF and sTNFR1, the rate of change (slope) was predictive for ICU length-of-stay (<3days). For exponential models, the rate constant was predictive for IL-1β and IL-8, whereas both the rate constant and starting value were predictive for IL-6. Combining kinetic model features with hourly data resulted in improved diagnostic performance for GM-CSF (AUROC=88% [95%CI:72-99]), IL-10 (AUROC=81% [95%CI:61-99]), and IL-8 (AUROC=72% [95%CI:50-94]). <h3>Conclusion</h3> Kinetic modeling can be utilized to accurately reflect protein biomarker behaviour during EVLP, and features extracted from these models improve the prediction of EVLP outcomes. This study represents an important framework to guide future research to improve the predictive utility of EVLP perfusate-derived biomarkers.

Religious culture and rural car ownership

Transportation policy generally restricts the predictors of car ownership to socio-economic variables without consideration of cultural factors. However, culture – the ideas, norms, and objects that a society shares – affects consumption decisions. Excluding this cultural context may limit consideration of potentially successful policy interventions. The research presented here statistically tests the impact of religious culture on rural car ownership in the United States by incorporating religious adherence rates into statistical models of countywide motorization rates. This research explores whether religious affiliation is a statistically significant predictor of car ownership and whether the addition of those religious variables results in a statistically significant improvement in model fit. The findings suggest that religious affiliation, expressed as countywide adherence rates, is a statistically significant predictor of car ownership and that its inclusion improves model fit; however, while statistically significant, that improvement in model fit is quite small. This research concludes that considering culture is a valuable direction for targeting car consumption policies aimed at curbing climate change. This research also demonstrates the need for additional exploration using disaggregated cultural data to better understand the import of this approach to policy development.

Risk Stratification With the Use of Coronary Computed Tomographic Angiography in Patients With Nonobstructive Coronary Artery Disease

ObjectivesThe purpose of this study was to develop a risk prediction model for patients with nonobstructive CAD. BackgroundAmong stable chest pain patients, most cardiovascular (CV) events occur in those with nonobstructive coronary artery disease (CAD). Thus, developing tailored risk prediction approaches in this group of patients, including CV risk factors and CAD characteristics, is needed. MethodsIn PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) computed tomographic angiography patients, a core laboratory assessed prevalence of CAD (nonobstructive 1% to 49% left main or 1% to 69% stenosis any coronary artery), degree of stenosis (minimal: 1% to 29%; mild: 30% to 49%; or moderate: 50% to 69%), high-risk plaque (HRP) features (positive remodeling, low-attenuation plaque, and napkin-ring sign), segment involvement score (SIS), and coronary artery calcium (CAC). The primary end point was an adjudicated composite of unstable angina pectoris, nonfatal myocardial infarction, and death. Cox regression analysis determined independent predictors in nonobstructive CAD. ResultsOf 2,890 patients (age 61.7 years, 46% women) with any CAD, 90.4% (n = 2,614) had nonobstructive CAD (mean age 61.6 yrs, 46% women, atherosclerotic cardiovascular disease [ASCVD] risk 16.2%). Composite events were independently predicted by ASCVD risk (hazard ratio [HR]: 1.03; p = 0.001), degree of stenosis (30% to 69%; HR: 1.91; p = 0.011), and presence of ≥2 HRP features (HR: 2.40; p = 0.008). Addition of ≥2 HRP features to: 1) ASCVD and CAC; 2) ASCVD and SIS; or 3) ASCVD and degree of stenosis resulted in a statistically significant improvement in model fit (p = 0.0036; p = 0.0176; and p = 0.0318; respectively). Patients with ASCVD ≥7.5%, any HRP, and mild/moderate stenosis had significantly higher event rates than those who did not meet those criteria (3.0% vs. 6.2%; p = 0.007). ConclusionsAdvanced coronary plaque features have incremental value over total plaque burden for the discrimination of clinical events in low-risk stable chest pain patients with nonobstructive CAD. This may be a first step to improve prevention in this cohort with the highest absolute risk for CV events.

Tax Specific Versus Generic Accounting-Based Textual Analysis and the Relationship with Effective Tax Rates: Building Context

A growing literature, typically using “bags of words” dictionaries, examines the information content of text in financial accounting disclosures. We generate context for our text analysis to help predict effective tax rates using two approaches. First, we create tax-specific, expert-derived, dictionaries and second, we generate the counts for those bags of words using text taken from tax-related discussions of the Form 10K, as opposed to its entirety. We find that using expertise provides more information than simply using general accounting and finance dictionaries. In addition, we find that generating general accounting text variable values from tax-related content in the Form 10K provides statistically significant improvement in model fit. Contrary to more generic accounting and finance word-based text analysis, we find that the signs on our positive and negative tax event dictionaries are different and are consistent with theoretical expectations through each of our modeled time periods.

Tax Specific versus Generic Accounting-Based Textual Analysis and the Relationship with Effective Tax Rates: Building Context

ABSTRACTA growing literature, typically using “bags of words” dictionaries, examines the information content of text in financial accounting disclosures. We generate context for our text analysis to help predict effective tax rates using two approaches. First, we create tax-specific, expert-derived, dictionaries and, second, we generate the counts for those bags of words using text taken from tax-related discussions of the Form 10-K, as opposed to its entirety. We find that using expertise provides more information than simply using general accounting and finance dictionaries. In addition, we find that generating general accounting text variable values from tax-related content in the Form 10-K provides statistically significant improvement in model fit. Contrary to more generic accounting and finance word-based text analysis, we find that the signs on our positive and negative tax event dictionaries are different and are consistent with theoretical expectations through each of our modeled time periods.

Validation of a brief measure of combat exposure among Canadian Armed Forces personnel.

Exposure to military combat is associated with mental health problems, including posttraumatic stress disorder (PTSD) and depression. To understand the effects of combat on adverse health outcomes, the sound measurement of combat experiences is required; however, many scales used in past research are lengthy. A brief measure of combat exposure benefits militaries by reducing the burden on respondents as well as administration time in post-deployment settings and large population-based health surveys. The current study sought to describe the psychometric properties of a brief measure of combat exposure among Canadian Armed Forces (CAF) personnel. Data from post-deployment screening were used to compare the psychometric properties of an 8-item scale with the full scale that it was derived from. The 8-item measure did not fit a one-factor solution well and did not offer a statistically significant improvement in model fit over the full 30-item measure. However, its association with increased odds of a number of health outcomes indicates that it could be useful as a brief measure of combat exposure in settings where using the full scale is not feasible. Brief measures of combat exposure are valuable for assessing events experienced during deployment among military personnel. Although the 8-item Combat Exposure Scale assessed in the current study represents a potentially useful measure for CAF personnel, further research is necessary to improve its fit.

Myocardial Fibrosis and Prognosis in Heart Transplant Recipients.

Myocardial fibrosis is a well-described histopathologic feature in heart transplant recipients. Whether myocardial fibrosis in heart transplant recipients is independently associated with clinical outcomes is unclear. We sought to determine whether myocardial fibrosis on late gadolinium enhancement cardiovascular magnetic resonance imaging in heart transplant recipients was independently associated with all-cause death or major adverse cardiac outcomes in the long-term. Using a cohort of consecutive heart transplant recipients that had cardiovascular magnetic resonance imaging, we determined the prevalence and the patterns of myocardial fibrosis and analyzed associations between myocardial fibrosis and a composite end point of all-cause death or major adverse cardiac events: retransplantation, nonfatal myocardial infarction, coronary revascularization, and heart failure hospitalization. One hundred and fifty-two heart transplant recipients (age, 54±15 years; 29% women; 5.0±5.4 years after heart transplantation) were included. Myocardial fibrosis was present in 18% (37% infarct pattern, 41% noninfarct pattern, and 22% both). Its prevalence was positively associated with cardiac allograft vasculopathy grade. With a median follow-up of 2.6 years, myocardial fibrosis was independently associated with all-cause death or major adverse cardiac events (hazard ratio, 2.88; 95% CI, 1.59-5.23; P<0.001) after adjustment for cardiac allograft vasculopathy, history of rejection, time since transplantation, left ventricular ejection fraction, and indexed right ventricular end-diastolic volume. Every 1% increase in myocardial fibrosis was independently associated with a 6% higher hazard for all-cause death or major adverse cardiac events (hazard ratio, 1.06; 95% CI, 1.03-1.09; P<0.001). The addition of myocardial fibrosis variables to models with cardiac allograft vasculopathy, history of rejection, time since transplantation, left ventricular ejection fraction, and indexed right ventricular end-diastolic volume resulted in significant improvements in model fit, suggesting incremental prognostic value. In heart transplant recipients, myocardial fibrosis is seen on late gadolinium enhancement cardiovascular magnetic resonance imaging in 18%. Both the presence and the extent of myocardial fibrosis are independently associated with the long-term risk of all-cause death or major adverse cardiac events.

Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample.

Genomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology typically relies on standard prediction procedures, such as genomic BLUP, that are not designed to accommodate population heterogeneity resulting from differences in marker effects across populations. In this study, we assayed different prediction procedures to capture marker-by-population interactions in genomic prediction models. Prediction procedures included genomic BLUP and two kernel-based extensions of genomic BLUP which explicitly accounted for population heterogeneity. To model population heterogeneity, dissemblance between populations was either depicted by a unique coefficient (as previously reported), or a more flexible function of genetic distance between populations (proposed herein). Models under investigation were applied in a diverse switchgrass sample under two validation schemes: whole-sample calibration, where all individuals except selection candidates are included in the calibration set, and cross-population calibration, where the target population is entirely excluded from the calibration set. First, we showed that using fixed effects, from principal components or putative population groups, appeared detrimental to prediction accuracy, especially in cross-population calibration. Then we showed that modeling population heterogeneity by our proposed procedure resulted in highly significant improvements in model fit. In such cases, gains in accuracy were often positive. These results suggest that population heterogeneity may be parsimoniously captured by kernel methods. However, in cases where improvement in model fit by our proposed procedure is null-to-moderate, ignoring heterogeneity should probably be preferred due to the robustness and simplicity of the standard genomic BLUP model.

Threshold Stochastic Volatility Models with Heavy Tails: A Bayesian Approach

This paper extends the threshold stochastic volatility (THSV) model specification proposed in So et al. (2002) and Chen et al. (2008) by incorporating thick-tails in the mean equation innovation using the scale mixture of normal distributions (SMN). A Bayesian Markov Chain Monte Carlo algorithm is developed to estimate all the parameters and latent variables. Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting via a computational Bayesian framework are considered. The MCMC-based method exploits a mixture representation of the SMN distributions. The proposed methodology is applied to daily returns of indexes from BM&F BOVESPA (BOVESPA), Buenos Aires Stock Exchange (MERVAL), Mexican Stock Exchange (MXX) and the Standar & Poors 500 (SP500). Bayesian model selection criteria reveals that there is a significant improvement in model fit for the returns of the data considered here, by using the THSV model with slash distribution over the usual normal and Student-t models. Empirical results show that the skewness can improve VaR and ES forecasting in comparison with the normal and Student-t models.

Models of upland species\u2019 distributions are improved by accounting for geodiversity

ContextRecent research suggests that novel geodiversity data on landforms, hydrology and surface materials can improve biodiversity models at the landscape scale by quantifying abiotic variability more effectively than commonly used measures of spatial heterogeneity. However, few studies consider whether these variables can account for, and improve our understanding of, species’ distributions.ObjectivesAssess the role of geodiversity components as macro-scale controls of plant species’ distributions in a montane landscape.MethodsWe used an innovative approach to quantifying a landscape, creating an ecologically meaningful geodiversity dataset that accounted for hydrology, morphometry (landforms derived from geomorphometric techniques), and soil parent material (data from expert sources). We compared models with geodiversity to those just using topographic metrics (e.g. slope and elevation) and climate data. Species distribution models (SDMs) were produced for ‘rare’ (N = 76) and ‘common’ (N = 505) plant species at 1 km2 resolution for the Cairngorms National Park, Scotland.ResultsThe addition of automatically produced landform geodiversity data and hydrological features to a basic SDM (climate, elevation, and slope) resulted in a significant improvement in model fit across all common species’ distribution models. Adding further geodiversity data on surface materials resulted in a less consistent statistical improvement, but added considerable conceptual value to many individual rare and common SDMs.ConclusionsThe geodiversity data used here helped us capture the abiotic environment’s heterogeneity and allowed for explicit links between the geophysical landscape and species’ ecology. It is encouraging that relatively simple and easily produced geodiversity data have the potential to improve SDMs. Our findings have important implications for applied conservation and support the need to consider geodiversity in management.

Monthly paleostreamflow reconstruction from annual tree-ring chronologies

Paleoclimate reconstructions are increasingly used to characterize annual climate variability prior to the instrumental record, to improve estimates of climate extremes, and to provide a baseline for climate-change projections. To date, paleoclimate records have seen limited engineering use to estimate hydrologic risks because water systems models and managers usually require streamflow input at the monthly scale. This study explores the hypothesis that monthly streamflows can be adequately modeled by statistically decomposing annual flow reconstructions. To test this hypothesis, a multiple linear regression model for monthly streamflow reconstruction is presented that expands the set of predictors to include annual streamflow reconstructions, reconstructions of global circulation, and potential differences among regional tree-ring chronologies related to tree species and geographic location. This approach is used to reconstruct 600 years of monthly streamflows at two sites on the Bear and Logan rivers in northern Utah. Nash-Sutcliffe Efficiencies remain above zero (0.26–0.60) for all months except April and Pearson’s correlation coefficients (R) are 0.94 and 0.88 for the Bear and Logan rivers, respectively, confirming that the model can adequately reproduce monthly flows during the reference period (10/1942 to 9/2015). Incorporating a flexible transition between the previous and concurrent annual reconstructed flows was the most important factor for model skill. Expanding the model to include global climate indices and regional tree-ring chronologies produced smaller, but still significant improvements in model fit. The model presented here is the only approach currently available to reconstruct monthly streamflows directly from tree-ring chronologies and climate reconstructions, rather than using resampling of the observed record. With reasonable estimates of monthly flow that extend back in time many centuries, water managers can challenge systems models with a larger range of natural variability in drought and pluvial events and better evaluate extreme events with recurrence intervals longer than the observed record. Establishing this natural baseline is critical when estimating future hydrologic risks under conditions of a non-stationary climate.

Can you buy work engagement? The relationship between pay, fringe benefits, financial bonuses and work engagement

What role do financial rewards play as predictors of work engagement? To address this question, based on the Job Demands-Resources Theory (JD-R), the relationship between financial rewards and work engagement on a large sample (N = 1201) of multi-occupational employees was investigated. Through three steps of hierarchical regression, salary, fringe benefits, and bonuses were added to a JD-R model predicting work engagement via job resources and job demands; however, this did not lead to any significant improvement in model fit, and all new predictors were not significant. It may be concluded that financial rewards do not explain an additional amount of variance in work engagement over job demands and job resources. These results offer insights into the relationship between financial rewards and work engagement, and suggest that there is insufficient evidence to claim that pay, benefits, and bonuses are related to employee work engagement level.