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BACKGROUND Premature ejaculation (PE) is a highly prevalent male sexual dysfunction. Its current definition, together with the most used diagnostic tools, does not include non-vaginal sexual intercourse such as anal sex, self-masturbation and/or partnered-masturbation, and other forms of sexual stimulation. However, diagnostic psychometry currently available is exclusively evaluating PE in the vaginal coitus. OBJECTIVES To validate a new tool, the Masturbatory Premature Ejaculation Diagnostic Tool (MPEDT), by assessing the control over ejaculation and its psychological effects during self-masturbation, rather than heterosexual vaginal intercourse. MATERIALS AND METHODS We studied 135 male patients aging from 18 to 40 years seeking medical care for PE in the Infertility and Sexual Medicine Department from June to September 2020. All the participants were asked to fill the PEDT and MPEDT questionnaires to estimate the PE symptoms during, respectively, intercourse and self-masturbation. The reliability/validity, the factor analysis of the tool, and the diagnostic sensitivity/specificity of MPEDT were calculated. RESULTS The overall Cronbach alpha was 0.884. In our adjusted model, both RMSEA (Root Mean Square Error of Approximation) and SRMR (Standardized Root Mean Square Residual) were lower than 0.08, while Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Non-Normed Fit Index (NNFI), and Comparative Fit Index (CFI) were higher than 0.9. The area under the ROC curve (AUC) is 0.943±0.015. The results suggest MPEDT points being ≤5 as "normal", ≥7 as "PE during self-masturbation" and 6 as "suspected PE", with the sensitivity of 91.9% and specificity of 88.1%. DISCUSSION An efficient diagnostic psychometric tool is needed for the individuals who, based on the impaired control over ejaculation during self-masturbation, may also suffer from PE during partnered intercourse. MPEDT is able to evaluate the existence of PE symptoms not necessarily during heterosexual intercourse but rather during self-masturbation, possibly aiding to the diagnosis, as well as planning and follow-up of a prompt therapeutical strategy. CONCLUSION MPEDT could be considered a new, objective and reliable diagnostic tool for the evaluation of the existence of PE symptoms. This article is protected by copyright. All rights reserved.

Allometric scaling relations can be observed in between molecular parameters. Hence, we looked for presence of such relation among sizes (i.e., lengths) of proteins and genes. Protein lengths exist in the literature as the number of amino acids. They can also be derived from the mRNA lengths. Here, we looked for allometric scaling relation by using such data and simultaneously, the data was compared with the sizes of genes and proteins that were obtained from our modified information-theoretic approach. Results implied presence of scaling relation in the calculated results. This was expected due to the implemented modification in the information-theoretic calculation. Relation in the literature-based data was lacking high goodness of fit value. It could be due to physical factors and selective pressures, which ended up in deviations of the literature-sourced values from those in the model. Genome size is correlated with cell size. Intracellular volume, which is related to the DNA size, would require certain number of proteins, the sizes of which can therefore be correlated with the protein sizes. Cell sizes, genome sizes, and average protein and gene sizes, along with the number of proteins, namely the expression levels of the genes, are the physical factors, and the molecular factors influence those physical factors. The selective pressures on those can act through the connection between those physical factors and limit the dynamic ranges. Biological measures could be prone to such forces and are likely to deviate from expected models, regardless of the validity of assumptions, unless those are also implemented in the models. Yet, present discrepancies could be pointing at the need for model improvement, data imperfection, invalid assumptions, etc. Still, current work highlights possible use of information-theoretic approach in allometric scaling relations' studies.

AIM: This study aims to clarify the construct and criterion-related validity of the Questionnaire for Older Senior Citizens (QO) during the COVID-19 pandemic. METHODS: This cross-sectional study was conducted in Japan between November 11, 2020 and January 10, 2021. Of the 1645 (63.5%) older adults who responded, data from 900 participants were analyzed. First, we conducted an exploratory factor analysis (EFA) among older-older adults (aged ≥75 years) and extracted the factors. Next, we conducted a confirmatory factor analysis (CFA) using structural equation modeling. We also conducted this analysis among younger-older adults (aged ≥65 and

The calculation of DFT (density functional theory) chemical shifts have become an important technique for the verification of a proposed structure. An easily calculated metric developed for proton and carbon chemical shifts of natural products and organic compounds, the calculated chemical shift index (CCSI), has been developed, which uses the deviation of each pair of calculated and experimental chemical shifts. The mean absolute deviation (MAD), which is commonly used as the goodness of fit metric for DFT calculated chemical shifts, can conceal large deviations in the calculated data. A classification strategy is also proposed for the CCSI to highlight when further assessment of the NMR data is required.

In recent years various probability models have been proposed for describing lifetime data. Increasing model flexibility is often sought as a means to better describe asymmetric and heavy tail distributions. Such extensions were pioneered by the beta-G family. However, efficient goodness-of-fit (GoF) measures for the beta-G distributions are sought. In this paper, we combine probability weighted moments (PWMs) and the Mellin transform (MT) in order to furnish new qualitative and quantitative GoF tools for model selection within the beta-G class. We derive PWMs for the Fr\’{e}chet and Kumaraswamy distributions; and we provide expressions for the MT, and for the log-cumulants (LC) of the beta-Weibull, beta-Fr\’{e}chet, beta-Kumaraswamy, and beta-log-logistic distributions. Subsequently, we construct LC diagrams and, based on the Hotelling’s $T^2$ statistic, we derive confidence ellipses for the LCs. Finally, the proposed GoF measures are applied on five real data sets in order to demonstrate their applicability.

The 60-item HEXACO (HEXACO-60) Personality Trait Inventory may be strategically more important for researchers compared to the 100-item HEXACO. The aim of this study was to assess the psychometric properties of the 60-item HEXACO questionnaire in the Serbian language. This cross-sectional study, conducted at three of five Faculties of Medicine during the summer semester of the 2016/2017 academic year, included 617 students. Relevant data were collected by an anonymous questionnaire. Cronbach's α and McDonald's ω coefficients for the total scale were .71 and .72, respectively. Apart from Agreeableness, all other domains showed an adequate level of internal consistency (both Cronbach's α and McDonald's ω coefficients were >.70). Random data eigenvalues obtained on the parallel analysis had higher values than initial exploratory factor analysis eigenvalues up to the sixth factor. Accordingly, it was concluded that six factors should be extracted from the Serbian HEXACO-60. These factors corresponded in item distribution with the original ones. Based on the confirmatory factor analysis, using both 60 items and 24 facets, overall goodness of fit of the Serbian HEXACO-60 was acceptable. Moreover, there were no domains with completely invalid indices, as there were at least two indices that were acceptable for each domain. The Serbian version of the HEXACO-60 has acceptable psychometric properties. This shorter version with 60 items may be more suitable for brief surveys or when the time allocated to fill in a questionnaire is limited.

This study examined the role of temperament type and gender of adolescents and teachers in adolescents’ well‐being in school. The sample consisted of 677 Finnish students and 56 classroom teachers. Parents rated adolescent temperament and teachers rated their own temperament in autumn of Grade 6. Self‐reports of school well‐being among adolescents were obtained in autumn and the fall of Grade 6. The results showed that being a girl and having resilient temperament type predicted higher school well‐being. In turn, boys with undercontrolled temperament, who were otherwise at risk for decreased school well‐being, particularly benefited from having a female teacher with resilient temperament. Overall, the results suggest that both adolescent temperament type and gender play important roles in adolescents’ well‐being in school. peerReviewed

In a paper published in 1939 in The Annals of Mathematical Statistics, Wald and Wolfowitz discussed the possible validity of a probability inequality between one- and two-sided coverage probabilities for the empirical distribution function. Twenty-eight years later, Vandewiele and Noé proved this inequality for Kolmogorov-Smirnov type goodness of fit tests. We refer to this type of inequality as one-two inequality. In this paper, we generalize their result for one- and two-sided union-intersection tests based on positively associated random variables and processes. Thereby, we give a brief review of different notions of positive association and corresponding results. Moreover, we introduce the notion of one-two dependence and discuss relationships with other dependence concepts. While positive association implies one-two dependence, the reverse implication fails. Last but not least, the Bonferroni inequality and the one-two inequality yield lower and upper bounds for two-sided acceptance/rejection probabilities which differ only slightly for significance levels not too large. We discuss several examples where the one-two inequality applies. Finally, we briefly discuss the possible impact of the validity of a one-two inequality on directional error control in multiple testing.

We evaluate the validity of a projection-based test checking linear models when the number of covariates tends to infinity, and analyze two gene expression datasets. We show that the test is still consistent and derive the asymptotic distributions under the null and alternative hypotheses. The asymptotic properties are almost the same as those when the number of covariates is fixed as long as p/n → 0 with additional mild assumptions. The test dramatically gains dimension reduction, and its numerical performance is remarkable.

Aim The purpose of this study was to determine the relationship between PSS, PH, FCP and QoL of oncology patients. Methods In this descriptive‐correlational study, 340 oncology patients were selected with convenience sampling method from the hospitals in Tehran 2018–2019. Data were collected using, “PSS,” “PH,” “FCP” and “European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, EORTC QLQ‐C30.” Data were analysed using descriptive and inferential statistics using SPSS21 and Amos. Results The direct effect and the total effect of PSS and FCP on QoL were significant (p .05) and the effect of PH on QoL was not significant (p = .96). The Root Mean Squares of Error Approximations (RMSEA), Non‐Normed Fit Index (NNFI), Comparative Fit Index (CFI) and Goodness of Fit Index (GFI) were estimated 0.07, 0.97, 0.98 and 0.91, respectively.

This study aimed to establish a feasible model for analysing factors affecting piglet litter performance at birth. Data of 61,984 litters were collected from 16 herds, and general linear model (GLM), multilevel Poisson regression model (MPM) and multilevel linear model (MLM) were established to compare their goodness of fit for these data. Influencing factors of piglet litter performance at birth were analysed using the established optimal model. Results showed the intraclass correlation coefficients of total born piglets (TBP), piglets born alive (PBA), low-birth-weight piglets (LBW), and average birth weight of piglets (ABW) reached 27.89%, 23.88%, 24.66% and 22.27%, respectively (p < .05). Akaike's information criterion and Bayesian information criterion in MLM of TBP, PBA, LBW and ABW were lower than those in GLM. Pearson residuals in MPM increased to nearly 1 after introduction of a discrete scale factor, and the p values in MPM were similar to those in MLM. Analyses of MLM indicated crossbred sows with good management supplemented with oregano essential oil and farrowing at warm season had higher TBA, PBA and ABW, but lower LBW than other sows (p < .05). In conclusion, MLM is superior to GLM and can replace MPM in analysing discrete data with hierarchical structure in pig production. More importantly, other potential influencing factors of litter performance at birth can be analysed using the established MLM in the future.

The chapter proposes an effective scheme for real‐time operation and protection of microgrids based on the distributed dynamic state estimation (DDSE) that applies on a single renewable DER or other components. First, the DDSE can be used for setting‐less component protection that applies dynamic state estimation on a component under protection with real‐time measurements and dynamic models of the component. Based on the results, the well‐known chi‐square test yields the confidence level that quantifies the goodness of fit of models to measurements, indicating the health status of the component. With this approach, renewable DERs in microgrids can be protected on an autonomous and adaptive basis. Meanwhile, the estimated state variables of each component are converted to phasor data with time tags and then collected to the DERMS of microgrids. These aggregated phasor data that are once filtered by the DDSE are input to the static state estimation in the DERMS along with unfiltered data sent from conventional meters, relays, and digital fault recorders, ultimately generating real‐time operating conditions of microgrids. This chapter also provides numerical simulations for comparing the DDSE‐based approach with the conventional centralized state estimation in terms of data accuracy and computational speeds.

In this paper it is shown that under the random effects generalized partial credit model for the measurement of a single latent variable by a set of polytomously scored items, the joint marginal probability distribution of the item scores has a closed-form expression in terms of item category location parameters, parameters that characterize the distribution of the latent variable in the subpopulation of examinees with a zero score on all items, and item-scaling parameters. Due to this closed-form expression, all parameters of the random effects generalized partial credit model can be estimated using marginal maximum likelihood estimation without assuming a particular distribution of the latent variable in the population of examinees and without using numerical integration. Also due to this closed-form expression, new special cases of the random effects generalized partial credit model can be identified. In addition to these new special cases, a slightly more general model than the random effects generalized partial credit model is presented. This slightly more general model is called the extended generalized partial credit model. Attention is paid to maximum likelihood estimation of the parameters of the extended generalized partial credit model and to assessing the goodness of fit of the model using generalized likelihood ratio tests. Attention is also paid to person parameter estimation under the random effects generalized partial credit model. It is shown that expected a posteriori estimates can be obtained for all possible score patterns. A simulation study is carried out to show the usefulness of the proposed models compared to the standard models that assume normality of the latent variable in the population of examinees. In an empirical example, some of the procedures proposed are demonstrated.

Color patterns are complex traits under selective pressures from conspecifics, mutualists, and antagonists. To evaluate the salience of a pattern or the similarity between colors, several visual models are available. Color discrimination models estimate the perceptual difference between any two colors. Their application to a diversity of taxonomic groups has become common in the literature to answer behavioral, ecological, and evolutionary questions. To use these models, we need information about the visual system of our beholder species. However, many color patterns are simultaneously subject to selective pressures from different species, often from different taxonomic groups, with different visual systems. Furthermore, we lack information about the visual system of many species, leading ecologists to use surrogate values or theoretical estimates for model parameters.Here, we present a modification of the segment classification method proposed by Endler (Biological Journal of the Linnean Society, 1990 41, 315–352): the normalized segment classification model (NSC). We explain its logic and use, exploring how NSC differs from other visual models. We also compare its predictions with available experimental data.Even though the NSC model includes no information about the visual system of the receiver species, it performed better than traditional color discrimination models when predicting the output of some behavioral tasks. Although vision scientists define color as independent of stimulus brightness, a likely explanation for the goodness of fit of the NSC model is that its distance measure depends on brightness differences, and achromatic information can influence the decision‐making process of animals when chromatic information is missing.Species‐specific models may be insufficient for the study of color patterns in a community context. The NSC model offers a species‐independent solution for color analyses, allowing us to calculate color differences when we ignore the intended viewer of a signal or when different species impose selective pressures on the signal., Color patterns are complex traits under a variety of biotic and abiotic selective pressures. In a community context, species‐specific models may be insufficient for the study of colour patterns. The NSC model offers a species‐independent solution for colour analyses, allowing ecologists to calculate colour differences when we ignore the intended viewer of a signal, or when different species impose selective pressures on the signal.

For the clinical development of a new drug, the determination of dose-proportionality is an essential part of the pharmacokinetic evaluations, which may provide early indications of non-linear pharmacokinetics and may help to identify sub-populations with divergent clearances. Prior to making any conclusions regarding dose-proportionality, the goodness-of-fit of the model must be assessed to evaluate the model performance. We propose the use of simulation-based visual predictive checks to improve the validity of dose-proportionality conclusions for complex designs. We provide an illustrative example and include a table to facilitate review by regulatory authorities.

In this paper, supply‐related and price data for several chickpea importing regions and supply‐related data for export competitor countries were encompassed into price transmission models to determine whether such factors are helpful for explaining the United States chickpea price variation. Specifically, the models included satellite‐based normalized difference vegetation index, a measure of growing conditions, and area planted estimates for importing regions of the Indian subcontinent and the Mediterranean, and area planted estimates for exporting competitors, Australia and Canada. Results show that inclusion of supply‐related information improved goodness of fit statistics in all models relative to base models that only include prices, providing evidence that such information is important for explaining United States chickpea price changes. The models with the highest goodness of fit statistics were those that accounted for (1) deteriorating growing conditions in the Mediterranean region coinciding with declining area planted in Canada and (2) concurrently deteriorating growing conditions and declining area planted in the Indian subcontinent. [EconLit citations: Q11, Q17]

In this paper, a new extension of Lomax distribution called the exponentiated power Lomax (EPOLO) distribution is proposed The proposed model accommodates monotonically increasing, decreasing, and unimodal hazard rates A full study of the proposed model parameters using four techniques is introduced and a simulation study is performed to examine the performance of the four methods of estimation for both complete and censored data EPOLO distribution is utilized to fit the number of revolutions of ball bearings, the tumor size of lung cancer patients, and the confirmed total deaths of the COVID-19 in Egypt and it provides better fits than some well-known distributions

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

When the same type of event is experienced by a subject more than once it is called recurrent event, which possess two important characteristics, namely "within-subject correlation" and "time-varying covariate." As a result, the traditional statistical methods do not work well on recurrent event data. Over the past few decades, many alternatives methods have been proposed for the analysis of recurrent event data. In this article, the six most prominent methods for recurrent event analysis have been compared on pediatric asthma data. Three variance corrected models (viz "Anderson and Gill [AG] model," "Prentice, William, and Peterson-Counting Process [PWP-CP] model," and "Prentice, William, and Peterson-Gap Time [PWP-GT] model") and three corresponding frailty variants (AG-frailty, PWP-CP-frailty, and PWP-GT-frailty) were compared using three mathematical criterion (AIC, BIC, and log-likelihood) and one graphical criteria (Cox-Snell goodness of fit, visual test). All model comparison indices showed the PWP-GT model as the most appropriate model on asthma data over other models. By using PWP-GT model, seven predictors of asthma exacerbation (viz "abdominal pain at previous visit," "Z5 (%) at previous visit," "diagnosis of asthma at previous visit," "calendar month of exacerbation," "history of maternal asthma," "monthly per capita income," and "emotional stress") were identified. The PWP-GT model was identified as the most appropriate model over other models on pediatrics asthma data.

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest., Species distribution models (SDMs) were developed for a taxonomically diverse suite of cetaceans using a robust set of systematic survey data within the California Current Ecosystem. Both boosted regression tress and generalized additive models were developed and their explanatory and predictive performance compared in light of features such as species distribution characteristics. Results have direct relevance for understanding the accuracy of SDMs and associated implications for marine management and conservation.

Evaluating the goodness of fit of logistic regression models is crucial to ensure the accuracy of the estimated probabilities. Unfortunately, such evaluation is problematic in large samples. Because the power of traditional goodness of fit tests increases with the sample size, practically irrelevant discrepancies between estimated and true probabilities are increasingly likely to cause the rejection of the hypothesis of perfect fit in larger and larger samples. This phenomenon has been widely documented for popular goodness of fit tests, such as the Hosmer-Lemeshow test. To address this limitation, we propose a modification of the Hosmer-Lemeshow approach. By standardizing the noncentrality parameter that characterizes the alternative distribution of the Hosmer-Lemeshow statistic, we introduce a parameter that measures the goodness of fit of a model but does not depend on the sample size. We provide the methodology to estimate this parameter and construct confidence intervals for it. Finally, we propose a formal statistical test to rigorously assess whether the fit of a model, albeit not perfect, is acceptable for practical purposes. The proposed method is compared in a simulation study with a competing modification of the Hosmer-Lemeshow test, based on repeated subsampling. We provide a step-by-step illustration of our method using a model for postneonatal mortality developed in a large cohort of more than 300 000 observations.

Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order‐invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to established approaches. We use the tests to evaluate generalized autoregressive conditional heteroskedasticity‐based multivariate density forecasts for a vector of stock market returns and macroeconomic forecasts from a Bayesian vector autoregression with time‐varying parameters.

OBJECTIVES To test hypothetical models relating caregivers' social status, knowledge, and self-efficacy to caregiver/child (C/C) oral health-related outcomes. METHODS One hundred fifty C/C pairs participated (recruitment = 87.7 percent). Three C/C outcomes were clinically assessed: "Oral self-care"; "Functional dentitions"; and "Dental treatment needs." Information about caregiver (CG) social status, knowledge, and self-efficacy was also gathered. Structural equation modeling tested measurement models (MMs) for CG social status, CG knowledge, and CG self-efficacy. The structural models (SMs) hypothesized causal paths among CG social status, CG knowledge, CG self-efficacy, and C/C oral health outcomes. RESULTS All three MMs had a good overall fit (nonsignificant χ2 estimates, Goodness of Fit Index >0.95, Normed Fit Index ~ >0.95, Confirmatory Fit Index >95, Root Mean Square Error Approximation

Purposes Multileaf collimator (MLC) positional accuracy during dynamic intensity modulation radiotherapy (IMRT) delivery is crucial for safe and accurate patient treatment. The deviations of individual leaf positions from its intended positions can lead to errors in the dose delivered to the patient and hence may adversely affect the treatment outcome. In this study, we propose a state-of-the-art machine learning (ML) method based on an artificial neural network (ANN) for accurately predicting the MLC leaf positional deviations during the dynamic IMRT treatment delivery priori using log file data. Methods Data of ten patients treated with sliding window dynamic IMRT delivery were retrospectively retrieved from a single-institution database. The patients' plans were redelivered with no patient on the couch using a Varian linear accelerator equipped with a Millennium 120 HD MLC system. Then the machine recorded log files data, a total of over 400 files containing 360 800 control points, were collected. A total of 14 parameters were extracted from the planning data in the log files such as leaf planned positions, dose fraction, leaf velocity, leaf moving status, leaf gap, and others. Next, we developed a feed-forward ANN architecture mapping the input parameters with the output to predict the MLC leaf positional deviations during the delivery priori. The proposed model was trained on 70% of the total data using the delivered leaf positional data as a target response. The trained model was then validated and tested on 30% of the available data. The model accuracy was evaluated using the mean squared error (MSE), regression plot, and error histogram. Results The deviations between the individual MLC planned and delivered positions can reach up to a few millimeters, with a maximum deviation of 1.2 mm. The predicted leaf positions at control points closely matched the delivered positions for all MLC leaves during the treatment delivery. The ANN model achieved a maximum MSE of 0.0001 mm2 (root MSE of 0.0097 mm) in predicting the leaf positions at control points of test data for each leaf. The correlation coefficient, that measures the goodness of fit, was perfect (R = 0.999) in all plots indicating an excellent agreement between the predicted and delivered MLC positions for the training, validation, and test data. Conclusions We successfully demonstrated a proposed ANN-based method capable of accurately predicting the individual MLC leaf positional deviations during the dynamic IMRT delivery priori. Our ML model based on ANN outperformed the reported accuracy in the literature of various ML models. The results of this study could be extended to actual application in the dose calculation/optimization, hence enhancing the gamma passing rate for patient-specific IMRT quality assurance.

We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modeled by a regime-switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness-of- fit procedure based on Cramer-von Mises statistics is proposed to select the appropriate number of regimes. Numerical experiments are performed to assess the validity of the proposed methodology. As an example of application, we evaluate a European put-on-max option on the returns of two assets. In order to facilitate the use of our methodology, we have built a R package HMMcopula available on CRAN.

BACKGROUND Evaluation of kidney function in newborns with hydronephrosis is important for clinical decisions. Dynamic contrast-enhanced (DCE) MRI can provide the necessary anatomical and functional information. Golden angle dynamic radial acquisition and compressed sensing reconstruction provides sufficient spatiotemporal resolution to achieve accurate parameter estimation for functional imaging of kidneys. However, bulk motion during imaging (rigid or nonrigid movement of the subject resulting in signal dropout) remains an unresolved challenge. PURPOSE To evaluate a motion-compensated (MoCo) DCE-MRI technique for robust evaluation of kidney function in newborns. Our method includes: 1) motion detection, 2) motion-robust image reconstruction, 3) joint realignment of the volumes, and 4) tracer-kinetic (TK) model fitting to evaluate kidney function parameters. STUDY TYPE Retrospective. SUBJECTS Eleven newborn patients (ages

Aims The objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications. Methods A systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies. Results A total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation. Conclusion A meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.

With massive wind power integration, the spatial distribution of electricity load centers and wind power plants make it plausible to study the inter-spatial dependence and temporal correlation for the effective working of the power system. In this paper, a novel multivariate framework is developed to study the spatio-temporal dependency using vine copula. Hourly resolution of load and wind power data obtained from a US regional transmission operator spanning 3 years and spatially distributed in 19 load and two wind power zones are considered in this study. Data collection, in terms of dimension, tends to increase in future, and to tackle this high-dimensional data, a reproducible sampling algorithm using vine copula is developed. The sampling algorithm employs k-means clustering along with singular value decomposition technique to ease the computational burden. Selection of appropriate clustering technique and copula family is realized by the goodness of clustering and goodness of fit tests. The paper concludes with a discussion on the importance of spatio-temporal modeling of load and wind power and the advantage of the proposed multivariate sampling algorithm using vine copula.

In a biomedical cohort study for assessing the association between an outcome variable and a set of covariates, it is common that some covariates can only be measured on a subgroup of study subjects. An important design question is which subjects to select into the subgroup towards increased statistical efficiency. When the outcome is binary, one may adopt a case-control sampling design or a balanced case-control design where cases and controls are further matched on a small number of complete discrete covariates. While the latter achieves success in estimating odds ratio (OR) parameters for the matching covariates, similar two-phase design options have not been explored for the remaining covariates, especially the incompletely collected ones. This is of great importance in studies where the covariates of interest cannot be completely collected. To this end, assuming that an external model is available relating the outcome and complete covariates, we propose a novel sampling scheme that oversamples cases and controls with worse goodness-of-fit based on the external model and further matches them on complete covariates similarly to the balanced design. We develop a pseudolikelihood method for estimating OR parameters. Through simulation studies and explorations in a real cohort study, we find that our design generally leads to reduced asymptotic variances of the OR estimates and the reduction for the matching covariates is comparable to that of the balanced design.

Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modelled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods. Here we compared modelled niche optima obtained from European-scale SDMs of 1,476 terrestrial vascular plant species with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species we first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. We then compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis. We found weak to moderate correlations in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modelled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM. Our method gives insight into the ecological realism of modelled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques.

Several ground-nesting bird species living in European farmlands are experiencing a persistent decline. Poorer reproductive success is suspected to be the main demographic driver. We developed a model to quantify to what extent such recent change may impact the viability of their populations. We took the grey partridge Perdix perdix, a typical farmland species, as a well-documented case study, but the model can be adapted to other species. We built a model based on our in-depth demographic knowledge of this species and the large database compiled from our long-term population monitoring programme. We took into account environmental and demographic stochasticity as well as density-dependence processes. We investigated the impact of recent changes in reproductive success on the viability of (unharvested) populations. For this purpose, we considered two periods: 1979–2004 as ‘Past’ and 2005–2014 as ‘Present’ (we used data of subsequent years to test the model’s goodness of fit). During the ‘Past’ period, the simulated population was increasing with a mean stochastic growth rate λmean = 1.01 (25% of decreasing trajectories), whereas λmean = 0.89 during the ‘Present’ period (74% of decreasing trajectories). We provide detailed results and discuss the possible environmental causes, with a focus on extreme weather events. In a further step, we used our model as a tool to investigate the impact of four hunting bag management strategies on exploited populations. The hunting bag was assumed to be an additive cause of mortality. Hunting leads to a decrease in λmean but the amplitude (from minor to substantial) depends upon the strategy and the setting-up of parameters. We deliver our model as a tool to support decision-making for sustainable population management and provide the script (ULM software) as Supplementary information.

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose-response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.