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We argue that in analyzing panel-data econometric models, researchers rely excessively on statistical criteria to determine model specification, treating it primarily as a matter of statistical inference. This inferential emphasis is most obvious in the common practice of using statistical tests (e.g., the Hausman test) to choose between fixed- and random-effects specifications, often ignoring the assumptions underpinning these tests. For instance, the Hausman test depends on the true within-panel (longitudinal) and between-panel (cross-sectional) parameters being equal. This assumption is often not justified, because longitudinal and cross-sectional variances and covariances may manifest different underpinning mechanisms. In addition to different mechanisms often resulting in different variables determining within and between effects, within and between variables may also have different meanings. To help researchers make theoretically informed choices, we formulate five questions that can guide researchers to think of model specification in a theoretically rigorous way. We examine these issues with examples from both general management and operations management research. Importantly, we argue that addressing the questions regarding model specification must involve primarily theoretical and contextual judgment, not statistical tests.

Profiling or evaluation of health care providers involves the application of statistical models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission, adjusted for patient case-mix characteristics. The nationally adopted method is based on random effects (RE) hierarchical logistic regression models. Although RE models are sensible for modeling hierarchical data, novel high dimensional fixed effects (FE) models have been proposed which may be well-suited for the objective of identifying sub-standard performance. However, there are limited comparative studies. Thus, we examine their relative performance, including the impact of inadequate case-mix adjustment.

Abstract We introduce community‐level basis function models (CBFMs) as an approach for spatiotemporal joint distribution modelling. CBFMs can be viewed as related to spatiotemporal latent variable models, where the latent variables are replaced by a set of pre‐specified spatiotemporal basis functions which are common across species. In a CBFM, the coefficients that link the basis functions to each species are treated as random slopes. As such, the CBFM can be formulated to have a similar structure to a generalised additive model. This allows us to adapt existing techniques to fit CBFMs efficiently. CBFMs can be used for a variety of reasons, such as inferring patterns of habitat use in space and time, understanding how residual covariation between species varies spatially and/or temporally, and spatiotemporal predictions of species‐ and community‐level quantities. A simulation study and an application to data from a bottom trawl survey conducted across the U.S. Northeast shelf show that CBFMs can achieve similar and sometimes better predictive performance compared to existing approaches for spatiotemporal joint species distribution modelling, while being computationally more scalable.

Information on the diameter increment of tree species is important for developing a sustainable forest harvesting plan and for reforestation. Fitting a forest growth model for diameter increment can be utilized to get the information. The objective of this study was to develop a predictive model for the annual diameter increment of a multispecies stand in the Sinharaja Rain Forest in Sri Lanka. To fulfill this objective, four linear mixedeffect models were fitted to predict the annual diameter at breast height (dbh) increment of trees by using dbh, the natural logarithm of dbh, and species as a random effect. The model that contained random coefficients for the intercept term, dbh and log (dbh) were selected as the best-fitted model based on the Akaike Information Criterion (AIC) value and the likelihood ratio test. Then, the bestfitted model was expanded by adding other subsequent variables that describe the effects of competition from surrounding trees, and a size structure component, which is the maximum dbh. The final model comprised of this information dbh, log(dbh), stocking density, maximum dbh, and competition from trees smaller than the subject tree. Using the best-fitted final model, the annual dbh increment of the 10 selected abundant species was calculated. It was noted that all the selected species have growth rates that increase rapidly at the initial stage, and then reach its unique maximum growth rate. These increment patterns reflected that different species followed different annual dbh increment patterns. Therefore, this final model can be consolidated into an effective empirical model to project the future growth of a tropical rainforest.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models. Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference. The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio. One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class. This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The step-stress accelerated degradation test (ADT) provides a feasible method for assessing the storage life of high-reliability, long-life products. However, this method results in a slower rate of performance degradation at the beginning of the test, significantly reducing the test efficiency. Therefore, this article proposes an assessment method for the step-down stress ADT that considers random effects and detection errors (SDRD). Firstly, a new Inverse Gaussian (IG) model is proposed. The model introduces the Gamma distribution to characterize the randomness of the product degradation path and uses the normal distribution to describe the detection errors of performance parameters. In addition, to solve the problem that the likelihood function of the IG model is complex and has no explicit expression, the Monte Carlo (MC) method is used to estimate unknown parameters of the model. This approach enhances computational accuracy and efficiency. Finally, to verify the effectiveness of the SDRD method, it is applied to the step-down stress ADT data from a specific missile tank to assess its storage life. Comparing the life assessment results of different methods, the conclusion shows that the SDRD method is more effective for assessing the storage life of high-reliability, long-life products.

Summary: In immuno‐oncology clinical trials, multiple immunological biomarkers are usually examined over time to comprehensively and appropriately evaluate the efficacy of treatments. Because predicting patients' future survival statuses on the basis of such recorded longitudinal information might be of great interest, joint modeling of longitudinal and time‐to‐event data has been intensively discussed as a toolkit to implement such a prediction. To achieve a desirable predictive performance, averaging over multiple candidate predictive models to account for the model uncertainty might be a more suitable statistical approach than selecting the single best model. Although Bayesian model averaging can be one of the approaches, several problems related to model weights with marginal likelihoods have been discussed. To address these problems, we here propose a Bayesian predictive model averaging (BPMA) method that uses Bayesian leave‐one‐out cross‐validation predictive densities to account for the subject‐specific and time‐dependent nature of the prediction. We examine the operating characteristics of the proposed BPMA method in terms of the predictive accuracy (ie, the calibration and discrimination abilities) in extensive simulation studies. In addition, we discuss the strengths and limitations of the proposed method by applying it to an immuno‐oncology clinical trial in patients with advanced ovarian cancer. [ABSTRACT FROM AUTHOR]

Actuaries use predictive modeling techniques to assess the loss cost on a contract as a function of observable risk characteristics. State-of-the-art statistical and machine learning methods are not well equipped to handle hierarchically structured risk factors with a large number of levels. In this paper, we demonstrate the data-driven construction of an insurance pricing model when hierarchically structured risk factors, contract-specific as well as externally collected risk factors are available. We examine the pricing of a workers' compensation insurance product with a hierarchical credibility model [Jewell, W. S. (1975). The use of collateral data in credibility theory: A hierarchical model. Laxenburg: IIASA], Ohlsson's combination of a generalized linear and a hierarchical credibility model [Ohlsson, E. (2008). Combining generalized linear models and credibility models in practice. Scandinavian Actuarial Journal2008(4), 301–314] and mixed models. We compare the predictive performance of these models and evaluate the effect of the distributional assumption on the target variable by comparing linear mixed models with Tweedie generalized linear mixed models. For our case-study the Tweedie distribution is well suited to model and predict the loss cost on a contract. Moreover, incorporating contract-specific risk factors in the model improves the predictive performance and the risk differentiation in our workers' compensation insurance portfolio. [ABSTRACT FROM AUTHOR]

The knowledge of the Fisher information is a fundamental tool to judge the quality of an experiment. Unlike in linear and generalized linear models without random effects, there is no closed form for the Fisher information in the situation of generalized linear mixed models, in general. To circumvent this problem, we make use of the quasi-information in this paper as an approximation to the Fisher information. We derive optimal designs based on the V-criterion, which aims to minimize the average variance of prediction of the mean response. For this criterion, we obtain locally optimal designs in two specific cases of a Poisson straight line regression model with either random intercepts or random slopes. [ABSTRACT FROM AUTHOR]

This article explores the factors that drive student mobility to developing and developed countries for studying abroad. Economic factors, such as employment opportunities and GDP per capita, are found to be the main determinants of global student flow. Other factors include the reputation of universities, cost of living, and cultural and social similarities. The study highlights the importance of considering return on investment, the value of a degree, and skill development when choosing a study destination. The findings provide policy implications for attracting foreign students and contribute to the existing knowledge on student mobility. [Extracted from the article]

In this paper, we propose joint random effects models for longitudinal mixed overdispersion binomial and normal responses where the overdispersion binomial response is inflated in zero point. Also, we propose a new parametric distribution forms called as the Zero-Inflated LogLindley-Binomial distribution for overdispersed binomial response with extra zeros. A LogLindley-Binomial distribution is obtained by compounding LogLindley and Binomial distributions. The random effect approach is used to investigate both of the correlation between responses. A Monte Carlo EM algorithm is utilized to obtain the parametric estimation of the models parameters. The models are illustrated by simulation study. Finally, these models are applied to air quality data, obtained from an observational study on Tehran where the correlated responses are the overdispersed binomial with extra zeros of particulate matter and normal response of AQI. The simultaneous effects of some covariates on both responses are also investigates. [ABSTRACT FROM AUTHOR]

The present paper introduces a random weights linear logistic test model for the measurement of individual differences in operation-specific practice effects within a single administration of a test. The proposed model is an extension of the linear logistic test model of learning developed by Spada (1977) in which the practice effects are considered random effects varying across examinees. A Bayesian framework was used for model estimation and evaluation. A simulation study was conducted to examine the behavior of the model in combination with the Bayesian procedures. The results demonstrated the good performance of the estimation and evaluation methods. Additionally, an empirical study was conducted to illustrate the applicability of the model to real data. The model was applied to a sample of responses from a logical ability test providing evidence of individual differences in operation-specific practice effects. [ABSTRACT FROM AUTHOR]

Why do inequalities in schooling persist, even in relatively egalitarian school systems? This article examines within school sorting as an explanation. We use classroom data on friendship networks in 480 European secondary schools and contrast comprehensive (England, Sweden) and tracked systems (Germany, Netherlands). Our question is to what extent comprehensive systems reduce achievement sorting at the level of (a) schools, (b) classrooms, and (c) friendships. Between-school variance in achievement is lower in comprehensive systems. However, this is counterbalanced by greater sorting within schools, between classrooms, and, especially, in friendship networks. Still, comprehensive schools create more equal environments for two reasons. First, the difference in between school sorting is larger than the difference in within school sorting. Second, within school sorting is less strongly related to social background characteristics. These findings help explain both why comprehensive schools produce more equal outcomes and how substantial inequality can nevertheless persist. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel spatial-temporal model with individual random effects characterized by a location-scale structure, which allows us to flexibly capture the pure influence of space-specific factors in a quantile regression framework. A hybrid two-stage estimation procedure is introduced for this model. The first stage proposes a Gaussian quasi-maximum likelihood estimator for the spatial-temporal effects, and the second constructs a weighted conditional quantile estimator, which we use to study the conditional quantiles of the random effects related to space-specific attributes. We verify the validity of the two-stage hybrid estimation, and establish the asymptotic properties of our estimators. The results of our simulation study indicate that the proposed estimation procedure performs well in different scenarios with finite-samples. Lastly, we apply the proposed method to data from a real case study on the air quality of China. [ABSTRACT FROM AUTHOR]

Multilevel modelling is a methodology that allows the consideration of variability in the level of the studied variables and the nature of the relationships between them, depending on the affiliation of study units to higher-level units (groups). Additionally, by dividing the studied population into groups, it is possible to explain part of the variability of the estimated characteristic using higher-level characteristics. The usefulness of multilevel modelling in estimating socioeconomic characteristics was investigated in the author's previous works. However, with large populations characterised by a multilevel structure, a significant drawback of this approach is its high computational complexity, often resulting in unacceptably long computation times. The main objective of the article is to propose a simplification in the algorithm of forward stepwise multilevel regression, allowing a significant reduction in the time required for variable selection in the model. The considerations will be illustrated by constructing a multilevel model to examine the determinants of daily flows related to employment based on the matrix of employment-related population flows developed from the 2021 National Census of Population and Housing (NSP 2021). [ABSTRACT FROM AUTHOR]

Dealing with fixed effects and random effects can be accomplished by combining statistical modeling and machine learning techniques. This paper discusses the modeling of fixed effects and random effects using a statistical machine-learning approach. We used the generalized mixed-effects trees (GMET), a tree-based mixed-effect model for dealing with response variables that belong to the exponential family of distributions. In this study, both simulation and actual/empirical data utilized the GMET method to discover data conditions that were appropriate for employing this approach. The simulation data was generated using different response variable generations, as well as different values of the variance of random effect and fixed effect coefficients. The findings indicated that the GMET performs similarly for different response variable generation scenarios. However, it performed better when the fixed effect value and the variance of random effects were large. When applied to the empirical data, the GMET method describes fixed effects and random effects and classifies household poverty status quite well based on the area under curve (AUC) value. It has also revealed that important variables for poverty classification are the number of household members, owning land, the type of main fuel used for cooking, and the main source of water used for drinking. In order to address the socioeconomic disparity that leads to poverty, the government may become concerned about these factors. In addition to that information, the use of regional typology as a random effect in the model has also contributed to the variation of household poverty status. Based on research, the fixed effects in mixed models do not need to be linear and GMET may be employed in grouped data structures, giving the GMET technique the ability to compete with other approaches/methods. [ABSTRACT FROM AUTHOR]

Mammography screening programs are aimed at reducing mortality due to breast cancer by detecting tumors at an early stage. There is currently interest in moving away from the age‐based screening programs, and toward personalized screening based on individual risk factors. To accomplish this, risk prediction models for breast cancer are needed to determine who should be screened, and when. We develop a novel approach using a (random effects) continuous growth model, which we apply to a large population‐based, Swedish screening cohort. Unlike existing breast cancer prediction models, this approach explicitly incorporates each woman's individual screening visits in the prediction. It jointly models invasive breast cancer tumor onset, tumor growth rate, symptomatic detection rate, and screening sensitivity. In addition to predicting the overall risk of invasive breast cancer, this model can make separate predictions regarding specific tumor sizes, and the mode of detection (eg, detected at screening, or through symptoms between screenings). It can also predict how these risks change depending on whether or not a woman will attend her next screening. In our study, we predict, given a future diagnosis, that the probability of having a tumor less than (as opposed to greater than) 10‐mm diameter, at detection, will be, on average, 2.6 times higher if a woman in the cohort attends their next screening. This indicates that the model can be used to evaluate the short‐term benefit of screening attendance, at an individual level. [ABSTRACT FROM AUTHOR]

There has been growing research interest in developing methodology to evaluate healthcare centers' performance with respect to patient outcomes. Conventional assessments can be conducted using fixed or random effects models, as seen in provider profiling. We propose a new method, using fusion penalty to cluster healthcare centers with respect to a survival outcome. Without any prior knowledge of the grouping information, the new method provides a desirable data‐driven approach for automatically clustering healthcare centers into distinct groups based on their performance. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. The validity of our approach is demonstrated through simulation studies, and its practical application is illustrated by analyzing data from the national kidney transplant registry. [ABSTRACT FROM AUTHOR]

We propose a new classified mixed model prediction (CMMP) procedure, called pseudo-Bayesian CMMP, that uses network information in matching the group index between the training data and new data, whose characteristics of interest one wishes to predict. The current CMMP procedures do not incorporate such information; as a result, the methods are not consistent in terms of matching the group index. Although, as the number of training data groups increases, the current CMMP method can predict the mixed effects of interest consistently, its accuracy is not guaranteed when the number of groups is moderate, as is the case in many potential applications. The proposed pseudo-Bayesian CMMP procedure assumes a flexible working probability model for the group index of the new observation to match the index of a training data group, which may be viewed as a pseudo prior. We show that, given any working model satisfying mild conditions, the pseudo-Bayesian CMMP procedure is consistent and asymptotically optimal both in terms of matching the group index and in terms of predicting the mixed effect of interest associated with the new observations. The theoretical results are fully supported by results of empirical studies, including Monte-Carlo simulations and real-data validation. [ABSTRACT FROM AUTHOR]

We consider three problems in high-dimensional linear mixed models. Without any assumptions on the design for the fixed effects, we construct asymptotic statistics for testing whether a collection of random effects is zero, derive an asymptotic confidence interval for a single random effect at the parametric rate n , and propose an empirical Bayes estimator for a part of the mean vector in ANOVA type models that performs asymptotically as well as the oracle Bayes estimator. We support our theoretical results with numerical simulations and provide comparisons with oracle estimators. The procedures developed are applied to the Trends in International Mathematics and Sciences Study (TIMSS) data. for this article are available online. [ABSTRACT FROM AUTHOR]

This article reviews Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, by Rabe-Hesketh and Skrondal (2022, Stata Press). [ABSTRACT FROM AUTHOR]

For estimation of heterogeneity variance τ2 in meta‐analysis of log‐odds‐ratio, we derive new mean‐ and median‐unbiased point estimators and new interval estimators based on a generalized Q statistic, QF, in which the weights depend on only the studies' effective sample sizes. We compare them with familiar estimators based on the inverse‐variance‐weights version of Q, QIV. In an extensive simulation, we studied the bias (including median bias) of the point estimators and the coverage (including left and right coverage error) of the confidence intervals. Most estimators add 0.5 to each cell of the 2×2 table when one cell contains a zero count; we include a version that always adds 0.5. The results show that: two of the new point estimators and two of the familiar point estimators are almost unbiased when the total sample size n≥250 and the probability in the Control arm (piC) is 0.1, and when n≥100 and piC is 0.2 or 0.5; for 0.1≤τ2≤1, all estimators have negative bias for small to medium sample sizes, but for larger sample sizes some of the new median‐unbiased estimators are almost median‐unbiased; choices of interval estimators depend on values of parameters, but one of the new estimators is reasonable when piC=0.1 and another, when piC=0.2 or piC=0.5; and lack of balance between left and right coverage errors for small n and/or piC implies that the available approximations for the distributions of QIV and QF are accurate only for larger sample sizes. [ABSTRACT FROM AUTHOR]

State-space assessment models (SSMs) have garnered attention recently because of their ability to estimate time variation in biological and fisheries processes such as recruitment, natural mortality, catchability, and selectivity. However, current SSMs cannot model time-varying growth internally nor accept length data, limiting their use. Here, we expand the Woods Hole Assessment Model to incorporate new approaches to modelling changes in growth using a combination of parametric and nonparametric approaches while fitting to length and weight data. We present these new features and apply them to data for three important Alaskan stocks with distinct data and model needs. We conduct a "self-test" simulation experiment to ensure the unbiasedness and statistical efficiency of model estimates and predictions. This research presents the first SSM that can be applied when length data are a key source of information, variation in growth is an essential part of the dynamics of the assessed stock, or when linking climate variables to growth in hindcasts or forecasts is relevant. Consequently, the state-space approach and growth estimation can be applied to more fish stocks worldwide, facilitating real-world applications and implementation of simulation experiments for performance evaluation of SSMs for the many stocks whose assessments rely on length data. [ABSTRACT FROM AUTHOR]

A functional time series approach is proposed for investigating spatial correlation in daily maximum temperature forecast errors for 111 cities spread across the U.S. The modelling of spatial correlation is most fruitful for longer forecast horizons, and becomes less relevant as the forecast horizon shrinks towards zero. For 6-day-ahead forecasts, the functional approach uncovers interpretable regional spatial effects, and captures the higher variance observed in inland cities versus coastal cities, as well as the higher variance observed in mountain and midwest states. The functional approach also naturally handles missing data through modelling a continuum, and can be implemented efficiently by exploiting the sparsity induced by a B-spline basis. The temporal dependence in the data is modeled through temporal dependence in functional basis coefficients. Independent first order autoregressions with generalized autoregressive conditional heteroskedasticity [AR(1)+GARCH(1,1)] and Student-t innovations work well to capture the persistence of basis coefficients over time and the seasonal heteroskedasticity reflecting higher variance in winter. Through exploiting autocorrelation in the basis coefficients, the functional time series approach also yields a method for improving weather forecasts and uncertainty quantification. The resulting method corrects for bias in the weather forecasts, while reducing the error variance. [ABSTRACT FROM AUTHOR]

Copyright of Ecosistemas y Recursos Agropecuarios is the property of Universidad Juarez Autonoma de Tabasco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the underwriting and pricing of nonlife insurance products, it is essential for the insurer to utilize both policyholder information and claim history to ensure profitability and proper risk management. In this paper, we apply a flexible regression model with random effects, called the Mixed Logit‐weighted Reduced Mixture‐of‐Experts, which leverages both policyholder information and their claim history, to categorize policyholders into groups with similar risk profiles, and to determine a premium that accurately captures the unobserved risks. Estimates of model parameters and the posterior distribution of random effects can be obtained by a stochastic variational algorithm, which is numerically efficient and scalable to large insurance portfolios. Our proposed framework is shown to outperform the classical benchmark models (Logistic and Lognormal GL(M)M) in terms of goodness‐of‐fit to data, while offering intuitive and interpretable characterization of policyholders' risk profiles to adequately reflect their claim history. [ABSTRACT FROM AUTHOR]

Many scientific and engineering challenges -- ranging from pharmacokineticdrug dosage allocation and personalized medicine to marketing mix (4Ps)recommendations -- require an understanding of the unobserved heterogeneity inorder to develop the best decision making-processes. In this paper, we developa hypothesis test and the corresponding p-value for testing for thesignificance of the homogeneous structure in linear mixed models. A robustmatching moment construction is used for creating a test that adapts to thesize of the model sparsity. When unobserved heterogeneity at a cluster level isconstant, we show that our test is both consistent and unbiased even when thedimension of the model is extremely high. Our theoretical results rely on a newfamily of adaptive sparse estimators of the fixed effects that do not requireconsistent estimation of the random effects. Moreover, our inference results donot require consistent model selection. We showcase that moment matching can beextended to nonlinear mixed effects models and to generalized linear mixedeffects models. In numerical and real data experiments, we find that thedeveloped method is extremely accurate, that it adapts to the size of theunderlying model and is decidedly powerful in the presence of irrelevantcovariates.

Profiling analysis aims to evaluate health care providers by modeling each provider's performance with respect to a patient outcome, such as unplanned hospital readmission. High-dimensional regression models are used in profiling to risk-adjust for patient case-mix covariates. Case-mix covariates typically ascertained from administrative databases are inherently error-prone. We examine the impact of case-mix measurement error (ME) on profiling models. The results show that even though the models' coefficient estimates are biased, this does not affect the estimation of standardized readmission ratio (SRR). However, ME leads to increased variation in SRR estimates and degrades the ability to identify under-performing providers.

Abstract Objective Random effects are often neglected when defining the control strategy for a biopharmaceutical process. In this article, we present a case study that highlights the importance of considering the variance introduced by random effects in the calculation of proven acceptable ranges (PAR), which form the basis of the control strategy. Methods Linear mixed models were used to model relations between process parameters and critical quality attributes in a set of unit operations that comprises a typical biopharmaceutical manufacturing process. Fitting such models yields estimates of fixed and random effect sizes as well as random and residual variance components. To form PARs, tolerance intervals specific to mixed models were applied that incorporate the random effect contribution to variance. Results We compared standardized fixed and random effect sizes for each unit operation and CQA. The results show that the investigated random effect is not only significant but in some unit operations even larger than the average fixed effect. A comparison between ordinary least squares and mixed models tolerance intervals shows that neglecting the contribution of the random effect can result in PARs that are too optimistic. Conclusions Uncontrollable effects such as week-to-week variability play a major role in process variability and can be modelled as a random effect. Following a workflow such as the one suggested in this article, random effects can be incorporated into a statistically sound control strategy, leading to lowered out of specification results and reduced patient risk.

Abstract Background Advantages of meta-analysis depend on the assumptions underlying the statistical procedures used being met. One of the main assumptions that is usually taken for granted is the normality underlying the population of true effects in a random-effects model, even though the available evidence suggests that this assumption is often not met. This paper examines how 21 frequentist and 24 Bayesian methods, including several novel procedures, for computing a point estimate of the heterogeneity parameter ( $${\tau }^{2}$$ τ 2 ) perform when the distribution of random effects departs from normality compared to normal scenarios in meta-analysis of standardized mean differences. Methods A Monte Carlo simulation was carried out using the R software, generating data for meta-analyses using the standardized mean difference. The simulation factors were the number and average sample size of primary studies, the amount of heterogeneity, as well as the shape of the random-effects distribution. The point estimators were compared in terms of absolute bias and variance, although results regarding mean squared error were also discussed. Results Although not all the estimators were affected to the same extent, there was a general tendency to obtain lower and more variable $${\tau }^{2}$$ τ 2 estimates as the random-effects distribution departed from normality. However, the estimators ranking in terms of their absolute bias and variance did not change: Those estimators that obtained lower bias also showed greater variance. Finally, a large number and sample size of primary studies acted as a bias-protective factor against a lack of normality for several procedures, whereas only a high number of studies was a variance-protective factor for most of the estimators analyzed. Conclusions Although the estimation and inference of the combined effect have proven to be sufficiently robust, our work highlights the role that the deviation from normality may be playing in the meta-analytic conclusions from the simulation results and the numerical examples included in this work. With the aim to exercise caution in the interpretation of the results obtained from random-effects models, the tau2() R function is made available for obtaining the range of $${\tau }^{2}$$ τ 2 values computed from the 45 estimators analyzed in this work, as well as to assess how the pooled effect, its confidence and prediction intervals vary according to the estimator chosen.

Abstract Hidden Markov models (HMMs) and their extensions are attractive methods for analysing ecological data where noisy, multivariate measurements are made of a hidden, ecological process, and where this hidden process is represented by a sequence of discrete states. Yet, as these models become more complex and challenging to understand, it is important to consider what pitfalls these methods have and what opportunities there are for future research to address these pitfalls. In this paper, we review five lesser known pitfalls one can encounter when using HMMs or their extensions to solve ecological problems: (a) violation of the snapshot property in continuous‐time HMMs; (b) biased inference from hierarchical HMMs when applied to temporally misaligned processes; (c) sensitive inference from using random effects to partially pool across heterogeneous individuals; (d) computational burden when using HMMs to approximate models with continuous state spaces; and (e) difficulty linking the hidden process to space or environment. This review is for ecologists and ecological statisticians familiar with HMMs, but who may be less aware of the problems that arise in more specialised applications. We demonstrate how each pitfall arises, by simulation or example, and discuss why this pitfall is important to consider. Along with identifying the problems, we highlight potential research opportunities and offer ideas that may help alleviate these pitfalls. Each of the methods we review are solutions to current ecological research problems. We intend for this paper to heighten awareness of the pitfalls ecologists may encounter when applying these more advanced methods, but we also hope that by highlighting future research opportunities, we can inspire ecological statisticians to weaken these pitfalls and provide improved methods.

This paper studies the financial risk management of resource allocation of listed commercial banks in my country, starting from the fundamental influence of lender credit rating in the internal rating system of commercial banks, and analyzes the influence of a large number of subjective factors contained in the bank’s credit rating system on the rating effect. Influence. A sort-response panel regression function model with random effects (including random intercept terms and random coefficients) is proposed, and the rationality of the existing bank credit rating system is tested and analyzed. The experimental results show that the model’s prediction accuracy rate is 48.27%, and the percentage of prediction level error within two levels reaches 86.8%. The application of this model can find the redundant indicators of the existing system, which are highly fitted with the actual situation and can achieve quite ideal forecasting results, and provide a forecasting reference for the weight setting, which provides a basis for the revision of the bank’s credit rating system.

Abstract Background The Minimising Young Children’s Anxiety through Schools (MY-CATS) trial is being conducted to determine whether an online evidence-based parent-guided cognitive behavioural therapy intervention in addition to usual school practice is effective and cost-effective compared with usual school practice in reducing anxiety disorders in children aged 4–7 deemed ‘at risk’ of anxiety disorders. This update article describes the detailed statistical analysis plan for the MY-CATS trial and reports a review of the underpinning sample size assumptions. Methods and design The MY-CATS study is a two-arm, definitive superiority pragmatic parallel group cluster randomised controlled trial in which schools will be randomised 1:1 to receive either the intervention (in addition to usual school practice) or the usual school practice only. This update to the (published) protocol provides a detailed description of the study methods, the statistical principles, the trial population and the planned statistical analyses, including additional analyses comprising instrumental variable regression and mediation analysis. Trial registration ISRCTN82398107 . Prospectively registered on 14 January 2021

Random effects contain crucial information to understand the variability of the processes under study in mixed-effects models with crossed random effects (MEMs-CR). Given that model selection makes all-or-nothing decisions regarding to the inclusion of model parameters, we evaluated if model averaging could deal with model uncertainty to recover random effects of MEMs-CR. Specifically, we analyzed the bias and the root mean squared error (RMSE) of the estimations of the variances of random effects using model averaging with Akaike weights and Bayesian model averaging with BIC posterior probabilities, comparing them with two alternative analytical strategies as benchmarks: AIC and BIC model selection, and fitting a full random structure. A simulation study was conducted manipulating sample sizes for subjects and items, and the variance of random effects. Results showed that model averaging, especially Akaike weights, can adequately recover random variances, given a minimum sample size in the modeled clusters. Thus, we endorse using model averaging to deal with model uncertainty in MEMs-CR. An empirical illustration is provided to ease the usability of model averaging.

Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24‐hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24‐hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape‐invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. When the model was fit separately by individual to the data from a small group of participants, two distinct 24‐hour rhythms were identified, with one being sinusoidal and the other being more complex with multiple peaks. Interestingly, the latent pattern associated with circadian variation (simple sinusoidal curve) had a similar phase across the three participants, while the more complex latent pattern reflecting diurnal variation differed across individual. The results suggested that this modeling framework can be used to separate 24‐hour rhythms into an endogenous circadian and one or more exogenous diurnal patterns in describing human metabolism. [ABSTRACT FROM AUTHOR]

The subject of this work is random coefficient regression models with only one observation per observational unit (individual). An analytical solution in form of optimality conditions is proposed for optimal designs for the prediction of individual random effect for a group of selected individuals. The behavior of optimal designs is illustrated by the example of linear regression models. [ABSTRACT FROM AUTHOR]

We introduce community‐level basis function models (CBFMs) as an approach for spatiotemporal joint distribution modelling. CBFMs can be viewed as related to spatiotemporal latent variable models, where the latent variables are replaced by a set of pre‐specified spatiotemporal basis functions which are common across species.In a CBFM, the coefficients that link the basis functions to each species are treated as random slopes. As such, the CBFM can be formulated to have a similar structure to a generalised additive model. This allows us to adapt existing techniques to fit CBFMs efficiently.CBFMs can be used for a variety of reasons, such as inferring patterns of habitat use in space and time, understanding how residual covariation between species varies spatially and/or temporally, and spatiotemporal predictions of species‐ and community‐level quantities.A simulation study and an application to data from a bottom trawl survey conducted across the U.S. Northeast shelf show that CBFMs can achieve similar and sometimes better predictive performance compared to existing approaches for spatiotemporal joint species distribution modelling, while being computationally more scalable. [ABSTRACT FROM AUTHOR]

A probabilistic seismic loss assessment of RC high-rise (RCHR) buildings designed according to Eurocode 8 and located in the Southern Euro-Mediterranean zone is presented herein. The loss assessment methodology is based on a comprehensive simulation approach which takes into account ground motion (GM) uncertainty, and the random effects in seismic demand, as well as in predicting the damage states (DSs). The methodology is implemented on three RCHR buildings of 20-story, 30-story and 40-story with a core wall structural system. The loss functions described by a cumulative lognormal probability distribution are obtained for two intensity levels for a large set of simulations (NLTHAs) based on 60 GM records with a wide range of magnitude (M), distance to source (R) and different site soil conditions (SS). The losses expressed in percent of building replacement cost for RCHR buildings are obtained. In the estimation of losses, both structural (S) and nonstructural (NS) damage for four DSs are considered. The effect of different GM characteristics (M, R and SS) on the obtained losses are investigated. Finally, the estimated performance of the RCHR buildings are checked to ensure that they fulfill limit state requirements according to Eurocode 8. [ABSTRACT FROM AUTHOR]

This article introduces a Laplace approximation to Bayesian inference in Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and heteroscedasticity, without having to transform the data. These data, which mainly consist of proportions or percentages of disjoint categories, are widely known as compositional data and are common in areas such as ecology, geology, and psychology. We provide both the theoretical foundations and a description of how Laplace approximation can be implemented in the case of Dirichlet regression. The article also introduces the package dirinla in the R-language that extends the R-INLA package, which can not deal directly with Dirichlet likelihoods. Simulation studies are presented to validate the good behavior of the proposed method, while a real data case-study is used to show how this approach can be applied. for this article are available online. [ABSTRACT FROM AUTHOR]

Reference intervals, or reference ranges, aid medical decision‐making by containing a pre‐specified proportion (e.g., 95%) of the measurements in a representative healthy population. We recently proposed three approaches for estimating a reference interval from a meta‐analysis based on a random effects model: a frequentist approach, a Bayesian posterior predictive interval, and an empirical approach. Because the Bayesian posterior predictive interval becomes wider to incorporate estimation uncertainty, it may systematically contain greater than 95% of measurements when the number of studies is small or the between study heterogeneity is large. The frequentist and empirical approaches also captured a median of less than 95% of measurements in this setting, and 95% confidence or credible intervals for the reference interval limits were not developed. In this update, we describe how one can instead use Bayesian methods to summarize the appropriate quantiles (e.g., 2.5th and 97.5th) of the marginal distribution of individuals across studies and construct a credible interval describing the estimation uncertainty in the lower and upper limits of the reference interval. We demonstrate through simulations that this method performs well in capturing 95% of values from the marginal distribution and maintains a median coverage of near 95% of the marginal distribution even when the number of studies is small, or the between‐study heterogeneity is large. We also compare the results of this method to those obtained from the three previously proposed methods in the original case study of the meta‐analysis of frontal subjective postural vertical measurements. [ABSTRACT FROM AUTHOR]

• COVID-19 has significantly impacted travel behavior. • This study examines changes in weekly driving hours, as well as the frequency of telecommuting, travel for medical purposes, and the use of ride-sharing and food delivery services. • Some changes had long-lasting effects while other behaviors reverted back toward pre-pandemic levels. • Changes also varied across individuals when considering demographic characteristics, area type, and political leanings. • Those individuals who were opposed to mandatory COVID-19 vaccines were less likely to change their travel behavior. The COVID-19 pandemic has significantly affected travel behavior, including the frequency and mode of travel, with the magnitude and nature of these effects varying over time. This study investigates the nature of these relationships by examining changes in various measures of travel behavior, including weekly driving hours, as well as the frequency of telecommuting, use of ride-sharing services, travel for medical purposes, and use of food delivery services. Self-reported travel data from a representative statewide survey of Michigan residents were used to assess changes in these metrics during the early stages of the pandemic, as well as one year thereafter. Random effects linear regression and ordered logit regression models were estimated and the findings show that various changes in behavior had long-lasting effects, while other behaviors generally reverted back toward pre-pandemic levels. In addition, these changes were found to vary across individuals. For example, significant differences were observed based on socio-demographic characteristics, between urban and rural areas, and amongst individuals with differing views on COVID-19 and related government interventions. In general, the pandemic tended to have less pronounced and sustained effects among younger adults as compared to older age groups. Further, those individuals who were opposed to mandatory COVID-19 vaccines were less likely to change their travel behavior, during both the early and latter stages of the pandemic. Changes were observed consistently across most of the travel metrics of interest. Among these, overall driving hours, travel for medical purposes, and ride-sharing were still lower during the latter stages of the pandemic, while telecommuting and the use of food delivery services reverted nearer to pre-pandemic levels. [ABSTRACT FROM AUTHOR]

Multilevel semicontinuous data occur frequently in medical, environmental, insurance and financial studies. Such data are often measured with covariates at different levels; however, these data have traditionally been modelled with covariate-independent random effects. Ignoring dependence of cluster-specific random effects and cluster-specific covariates in these traditional approaches may lead to ecological fallacy and result in misleading results. In this paper, we propose Tweedie compound Poisson model with covariate-dependent random effects to analyze multilevel semicontinuous data where covariates at different levels are incorporated at relevant levels. The estimation of our models has been developed based on the orthodox best linear unbiased predictor of random effect. Explicit expressions of random effects predictors facilitate computation and interpretation of our models. Our approach is illustrated through the analysis of the basic symptoms inventory study data where 409 adolescents from 269 families were observed at varying number of times from 1 to 17 times. The performance of the proposed methodology was also examined through the simulation studies. [ABSTRACT FROM AUTHOR]

In this paper, we propose tests for the existence of random effects and interactions for two-way models with dependent errors. We prove that the proposed tests are asymptotically distribution-free which have asymptotically size τ and are consistent. We elucidate the nontrivial power under the local alternative when a sample size tends to infinity and the number of groups is fixed. A simulation study is performed to investigate the finite-sample performance of the proposed tests. In the real data analysis, we apply our tests to the daily log-returns of 24 stock prices from six countries and four sectors. We find that there is no strong evidence to support the existence of substantial differences in the log-return across countries, nor to the existence of interactions between countries and sectors. However, there exists random effect differences in the daily log-return series across different sectors. [ABSTRACT FROM AUTHOR]

Although most statistical methods for the analysis of longitudinal data have focused on retrospective models of association, new advances in mobile health data have presented opportunities for predicting future health status by leveraging an individual's behavioral history alongside data from similar patients. Methods that incorporate both individual‐level and sample‐level effects are critical to using these data to its full predictive capacity. Neural networks are powerful tools for prediction, but many assume input observations are independent even when they are clustered or correlated in some way, such as in longitudinal data. Generalized linear mixed models (GLMM) provide a flexible framework for modeling longitudinal data but have poor predictive power particularly when the data are highly nonlinear. We propose a generalized neural network mixed model that replaces the linear fixed effect in a GLMM with the output of a feed‐forward neural network. The model simultaneously accounts for the correlation structure and complex nonlinear relationship between input variables and outcomes, and it utilizes the predictive power of neural networks. We apply this approach to predict depression and anxiety levels of schizophrenic patients using longitudinal data collected from passive smartphone sensor data. [ABSTRACT FROM AUTHOR]

We consider model selection for linear mixed‐effects models with clustered structure, where conditional Kullback–Leibler (CKL) loss is applied to measure the efficiency of the selection. We estimate the CKL loss by substituting the empirical best linear unbiased predictors (EBLUPs) into random effects with model parameters estimated by maximum likelihood. Although the BLUP approach is commonly used in predicting random effects and future observations, selecting random effects to achieve asymptotic loss efficiency concerning CKL loss is challenging and has not been well studied. In this paper, we propose addressing this difficulty using a conditional generalized information criterion (CGIC) with two tuning parameters. We further consider a challenging but practically relevant situation where the number, m$$ m $$, of clusters does not go to infinity with the sample size. Hence the random‐effects variances are not consistently estimable. We show that via a novel decomposition of the CKL risk, the CGIC achieves consistency and asymptotic loss efficiency, whether m$$ m $$ is fixed or increases to infinity with the sample size. We also conduct numerical experiments to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of simultaneous variable selection and estimation in the random‐intercepts model with the first‐order lag response. This type of model is commonly used for analyzing longitudinal data obtained through repeated measurements on individuals over time. This model uses random effects to cover the intra‐class correlation, and the first lagged response to address the serial correlation, which are two common sources of dependency in longitudinal data. We demonstrate that the conditional likelihood approach by ignoring correlation among random effects and initial responses can lead to biased regularized estimates. Furthermore, we demonstrate that joint modeling of initial responses and subsequent observations in the structure of dynamic random‐intercepts models leads to both consistency and Oracle properties of regularized estimators. We present theoretical results in both low‐ and high‐dimensional settings and evaluate regularized estimators' performances by conducting simulation studies and analyzing a real dataset. Supporting information is available online. [ABSTRACT FROM AUTHOR]