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Structural equation modeling (SEM) is a widespread approach to test substantive hypotheses in psychology and other social sciences. However, most studies involving structural equation models neither report statistical power analysis as a criterion for sample size planning nor evaluate the achieved power of the performed tests. In this tutorial, we provide a step-by-step illustration of how a priori, post hoc, and compromise power analyses can be conducted for a range of different SEM applications. Using illustrative examples and the R package semPower, we demonstrate power analyses for hypotheses regarding overall model fit, global model comparisons, particular individual model parameters, and differences in multigroup contexts (such as in tests of measurement invariance). We encourage researchers to yield reliable-and thus more replicable-results based on thoughtful sample size planning, especially if small or medium-sized effects are expected. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The literature review highlighted the impacts of drivers’ behavior on passengers’ attitudes in the choice of seatbelt usage. However, limited studies have been done to determine those impacts. Studying the passengers’ seatbelt use is especially needed to find out why passengers choose not to buckle up, and consequently it helps decision makers to target appropriate groups. So, this study was conducted to find drivers’ characteristics that might impact the passenger’s seatbelt use, in addition to other passengers’ characteristics themselves. While performing any analysis, it is important to use a right statistical model to achieve a less biased point estimate of the model parameters. The latent class multinomial logit model (LC-MNL) can be seen as an alternative to the mixed logit model, replacing the continuous with a discrete distribution, by capturing possible heterogeneity through membership in various clusters. In this study, instead of a response to the survey or crash observations, we employed a real-life observational data for the analysis. Results derived from the analysis reveal a clear indication of heterogeneity across individuals for almost all parameters. Various socio-demographic variables for class allocation and models with different latent numbers were considered and checked in terms of goodness of fit. The results indicated that a class membership with three factors based on vehicle type would result in a best fit. The results also highlighted the significant impacts of driver seatbelt status, time of a day, distance of traveling, vehicle type, and driver gender, instead of passenger gender, as some of the factors impacting the passengers’ choice of seatbelt usage. In addition, it was found that the belting status of passengers is positively associated with the belting condition of drivers, highlighting the psychological behavioral impact of drivers on passengers. Extensive discussion has been made regarding the implications of the findings.

Growth is a fundamental aspect of a living organism. Growth curves play an important role in explaining the complex dynamics of growth trajectories. The development of a large class of growth models provides more choices to explain complex growth dynamics. However, identifying a suitable growth curve from a broad class of growth models becomes a challenging task. Relative Growth Rate (RGR) is the most popular measure in the growth-related study. It serves many purposes in growth curve literature, including constructing any goodness-of-fit index of some growth dynamics. However, the goodness-of-fit test based on RGR is restricted to only simple growth models. This study aims to develop a new growth rate function, instantaneous maturity rate (IMR), which can play an important role in identifying growth models. We have explored that the measure has synergy in mathematical form with IMR. However, unlike the hazard rate, IMR is a random variable when the size/RGR variable is stochastic. We have derived the exact and asymptotic distribution of this measure under the Gaussian setup of both the size and RGR variables. We have constructed a goodness-of-fit test for the extended Gompertz growth model based on the instantaneous maturity rate. We have checked the performance of the test through simulation studies as well as real data. AMS 2010 subject classifications: 62Mxx, 92Bxx, 62P10

To distinguish between respondents that have attended to/ignored an attribute in discrete choice experiments (DCE), Hess and Hensher (HH) apply the coefficient of variation of the conditional distribution, setting a threshold of 2 as a conservative rule of thumb. This paper develops an analytical framework (piecewise regression analysis — PWRA) to refine the HH approach, offering a flexible method to identify attribute non-attendance (ANA) in highly context-dependent DCE. It is empirically tested on a datasetusedtovalueagriculturalpublicgoods.Theresultssuggestthattheidentification of non-attendance and goodness of fit of different random parameter logit models that accommodate ANA are better when the framework developed in this research is applied. When comparing welfare estimates from the HH and PWRA approach, significant differences are observed. Consequently, the flexibility of the PWRA notably contributes to revealing context-specific ANA patterns that can help to provide more accurate welfare measures and therefore policy recommendations.

Modeling loss in the case of default is a crucial task for financial institutions to support the decision making process in the risk management framework. It has become an inevitable part of modern debt collection strategies to keep promising loans on the banking book and to write off those that are not expected to be recovered at a satisfactory level. Research tends to model Loss Given Default directly or to decompose it based on the dependent variable distribution. Such an approach neglects the patterns which exist beneath the recovery process and are mainly driven by the activities made by collectors in the event of default. To overcome this problem, we propose a decomposition of the LGD model that integrates cures, partial recoveries, and write-offs into one equation, defined based on common collection strategies. Furthermore, various levels of data aggregation are applied to each component to reflect the domain that influences each stage of the default process. To assess the robustness of our approach, we propose a comparison with two benchmark models on two different datasets. We assess the goodness of fit on out-of-sample data and show that the proposed decomposition is more effective than state-of-the-art methods, maintaining a strong level of interpretability.

The purpose of this research is to analyze the relationship of causal factors and the error correction ability of these factors, including economic growth, government policy, and environmental growth, under the energy law of Thailand. This research proposes a novel structural equation model called the “Structural Equation Model based on Autoregressive Integrated Moving Average with Observed Variables (SEM-based on the ARIMAXi model)”. The validity of this proposed model is confirmed upon testing the goodness of fit and white noise property. Upon analysis, this research reveals that the SEM-based on the ARIMAXi (1,1,1) model is composed of causal factors, where economic growth was found to be related to environmental growth with an influential impact rate of 0.76 per cent at a confidence interval of 99 per cent. The relationship between economic growth and government policy was also detected to have a 0.51 per cent impact at a confidence interval of 99 per cent. Furthermore, this research identifies government policy as being related to environmental growth with an impact of 0.19 per cent at a confidence interval of 99 per cent. In addition, this research indicates that economic growth is the strongest factor with an error correction ability of -0.74, followed by government policy and environmental growth factors with an error correction ability of -0.45 and -0.06, respectively. With the weakest error correction ability in environmental growth, this suggests that the government must intervene in taking action to preserve the environment.Keywords: Structural Equation Model, greenhouse gases, carrying capacity, energy consumption, Error Correction Mechanism, causal factors.JEL Classifications: P28, Q42, Q43, Q47, Q48DOI: https://doi.org/10.32479/ijeep.9995

The paper considers the pricing of credit default swaps (CDSs) using a revised version of the credit risk model proposed in Cathcart and El-Jahel (2003). Default occurs either the first time a signaling process breaches a threshold barrier or unexpectedly at the first jump of a Cox process. The intensity of default depends on the risk-free interest rate, which follows a Vasicek process, instead of a Cox-Ingersoll-Ross process as in the original model. This offers two advantages. On the one hand, it allows us to account for negative interest rates which are recently observed, on the other hand, it simplifies the formula for pricing CDSs. The goodness of fit of the model is tested using a dataset of CDS credit spreads related to European companies. The results obtained show a rather satisfactory agreement between theoretical predictions and market data, which is identical to the one obtained with the original model. In addition, the values of the calibrated parameters result to be stable over time and the semi-closed form solution ensures a very fast implementation.

Vine copula, constructed from bivariate copulas, provides great flexibility in modelling complex high-dimensional dependence. When applied to multi-population mortality modelling, vine copula yields significant improvement over traditional multivariate copulas. In this paper, we propose to capture time-varying features in mortality dependence with dynamic regular vine (R-vine) copula which is built from bivariate copulas with time-varying dependence parameters. We develop two dependence dynamics for R-vine copulas and illustrate the selection and estimation of dynamic R-vine copulas using mortality data from eight populations. The estimated R-vine copulas using the proposed dependence dynamics are shown to yield better goodness of fit than both static and regime-switching vine copulas. We further demonstrate the simulation of mortality paths using dynamic R-vine copulas and examine the impact of vine copula choice on the assessed effectiveness of longevity hedge.

While ecologists know that models require assumptions, the consequences of their violation become vague as model complexity increases. Integrated population models (IPMs) combine several datasets to inform a population model and to estimate survival and reproduction parameters jointly with higher precision than is possible using independent models. However, accuracy actually depends on an adequate fit of the model to datasets. We first investigated bias of parameters obtained from integrated population models when specific assumptions are violated. For instance, a model may assume that all females reproduce although there are non-breeding females in the population. Our second goal was to identify which diagnostic tests are sensitive to detect violations of the assumptions of IPMs. We simulated data mimicking a short- and a long-lived species under five scenarios in which a specific assumption is violated. For each simulated scenario, we fitted an IPM that violates the assumption (simple IPM) and an IPM that does not violate each specific assumption. We estimated bias and uncertainty of parameters and performed seven diagnostic tests to assess the fit of the models to the data. Our results show that the simple IPM was quite robust to violation of many assumptions and only resulted in small bias of the parameter estimates. Yet, the applied diagnostic tests were not sensitive to detect such small bias. The violation of some assumptions such as the absence of immigrants resulted in larger bias to which diagnostic tests were more sensitive. The parameters informed by the least amount of data were the most biased in all scenarios. We provide guidelines to identify misspecified models and to diagnose the assumption being violated. Simple models should often be sufficient to describe simple population dynamics, and when data are abundant, complex models accounting for specific processes will be able to shed light on specific biological questions. &nbsp

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.

Exponential-family random graph models (ERGMs) are widely used in social network analysis when modelling data on the relations between actors. ERGMs are typically interpreted as a snapshot of a network at a given point in time or in a final state. The recently proposed Latent Order Logistic model (LOLOG) directly allows for a latent network formation process. We assess the real-world performance of these models when applied to typical networks modelled by researchers. Specifically, we model data from an ensemble of articles in the journal Social Networks with published ERGM fits, and compare the ERGM fit to a comparable LOLOG fit. We demonstrate that the LOLOG models are, in general, in qualitative agreement with the ERGM models, and provide at least as good a model fit. In addition, they are typically faster and easier to fit to data, without the tendency for degeneracy that plagues ERGMs. Our results support the general use of LOLOG models in circumstances where ERGMs are considered.

The Campbell and Vuolteenaho (Am Econ Rev 94(5):1249–1275, 2004) two–beta model decomposes the systematic risk in the sensitivity of cash flow and discount rate change. We employed this model, which we call the Two Beta Decomposition Model (TBDM) and found that this model is useful to compute the cost of capital for unlisted companies (UCs) via a proxy from listed companies. This model includes not only the accounting return reaction to long-term changes in consumption, but also links fundamental reactions to temporal changes in risk aversion. We test this model along with three traditional alternatives that are potentially useful in computing the cost of equity for UCs: accounting betas (AB), unlevered betas (UB), and operational betas (OB). Our results show that AB, UB and TBDM can partially explain the cross-sectional variations of stock returns. Additionally, using a series of non-parametric ranking test along with several statistics of goodness of fit, we found that the TBDM is the model that produces the best fit among competing models followed by the UB which is currently the most used among proxy methods.

Background/Objectives: To analyze and investigate the relationships for the inflation rate, interest rate, literacy rate and unemployment rate of Pakistan with a high level of accuracy. Methods/Statistical analysis: Method of nonlinear least squares have been adopted to fit the nonlinear regression models to estimate the present and future trends for the inflation rate, interest rate, literacy rate and unemployment rate of Pakistan based on the data from the year 2000 to 2019. Various nonlinear regression models were tested by changing their degree and number of coefficients. For each trial, the goodness of fit was set at a 95% confidence level. The best regression models were selected on basis of goodness of fit, the correlation with the present data and the logical trend of future forecasts. Findings: The proposed nonlinear regression models are quite different from the conventional linear and nonlinear polynomial regression models. The fitted and forecasting graphs show very realistic results that can be used by policymakers with good accuracy. Novelty/Applications: Since the periodic abrupt changes in the quantitative response variables like inflation rate, interest rate, literacy rate and the unemployment rate of Pakistan have been smoothly incorporated; therefore, the government or other socio-economic practitioners may use the results for future planning and management of the resources depending upon these factors. Keywords: Inflation rate; literacy rate; interest rate; unemployment rate; nonlinear regression; forecasting

Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death tolls and economy loss very hard, is more complex and contagious than its precedent diseases. The complexity comes mostly from the emergence of asymptomatic patients and relapse of the recovered patients which were not commonly seen during SARS outbreaks. These new characteristics pertaining to COVID-19 were only discovered lately, adding a level of uncertainty to the traditional SEIR models. The contribution of this paper is that for the COVID-19 epidemic, which is infectious in both the incubation period and the onset period, we use neural networks to learn from the actual data of the epidemic to obtain optimal parameters, thereby establishing a nonlinear, self-adaptive dynamic coefficient infectious disease prediction model. On the basis of prediction, we considered control measures and simulated the effects of different control measures and different strengths of the control measures. The epidemic control is predicted as a continuous change process, and the epidemic development and control are integrated to simulate and forecast. Decision-making departments make optimal choices. The improved model is applied to simulate the COVID-19 epidemic in the United States, and by comparing the prediction results with the traditional SEIR model, SEAIRD model and adaptive SEAIRD model, it is found that the adaptive SEAIRD model's prediction results of the U.S. COVID-19 epidemic data are in good agreement with the actual epidemic curve. For example, from the prediction effect of these 3 different models on accumulative confirmed cases, in terms of goodness of fit, adaptive SEAIRD model (0.99997) approximate to SEAIRD model (0.98548) > Classical SEIR model (0.66837); in terms of error value: adaptive SEAIRD model (198.6563) < < SEAIRD model(4739.8577) < < Classical SEIR model (22,652.796); The objective of this contribution is mainly on extending the current spread prediction model. It incorporates extra compartments accounting for the new features of COVID-19, and fine-tunes the new model with neural network, in a bid of achieving a higher level of prediction accuracy. Based on the SEIR model of disease transmission, an adaptive model called SEAIRD with internal source and isolation intervention is proposed. It simulates the effects of the changing behaviour of the SARS-CoV-2 in U.S. Neural network is applied to achieve a better fit in SEAIRD. Unlike the SEIR model, the adaptive SEAIRD model embraces multi-group dynamics which lead to different evolutionary trends during the epidemic. Through the risk assessment indicators of the adaptive SEAIRD model, it is convenient to measure the severity of the epidemic situation for consideration of different preventive measures. Future scenarios are projected from the trends of various indicators by running the adaptive SEAIRD model.

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.

We introduce the special section on nonparametric item response theory (IRT) in Quality of Life Research. Starting from the well-known Rasch model, we provide a brief overview of nonparametric IRT models and discuss the assumptions, the properties, and the investigation of goodness of fit. We provide references to more detailed texts to help readers getting acquainted with nonparametric IRT models. In addition, we show how the rather diverse papers in the special section fit into the nonparametric IRT framework. Finally, we illustrate the application of nonparametric IRT models using data from a questionnaire measuring activity limitations in walking. The real-data example shows the quality of the scale and its constituent items with respect to dimensionality, local independence, monotonicity, and invariant item ordering.

Strong empirical links exist between the number of years spent schooling and earnings. How­ever, the relationship may be masked due to the effect of unobserved factors that influence both wages and schooling. Two of the main econometric models, namely fixed-effects and se­lection-effects, used to analyse returns to schooling were compared using monozygotic and di­zygotic twins’ datasets in Ghana. The efficiency of the models was assessed based on the stan­dard errors associated with the return to schooling estimates. Goodness of fit measures was used as a basis for comparison of the performance of the two models. The results revealed that based on their standard errors, the regression estimates from the selection effects model (MZ = 0.1014±0.0197; DZ = 0.0947±0.0095) were more efficient than the regression estimates from the fixed-effects model (MZ = 0.1115±0.0353; DZ = 0.082±0.0127). However, the AICc values of the fixed effects model (MZAICc = 57.8 and DZAICc = 105.4) were smaller than the AICc values of the selection effects model (MZAICc = 151.6 and DZAICc = 221.6). Findings from the study indicate that, although both models produced consistent estimates of the economic returns to schooling, the fixed effects model provided a better fit to the twins’ data set.

Rater fit analyses provide insight into the degree to which rater judgments correspond to expected properties, as defined within a measurement framework. Parametric models such as the Rasch model p...

This study aims to compare the magnitude of operational risk losses between the Basic Indicator Approach (BIA) method, and the loss distribution model in the Advanced Measurement Approach (AMA) approach so as to provide a more realistic picture for banks to determine the operational risk capital burden that must be provided based on the causes Operational risks are as follows Internal Process, Human and External Events. Measurement of operational risk capital burden by the AMA method is the determination of frequency of loss distribution, determination of severity of loss distribution, testing with goodness of fit test, then compilation of aggregated loss distribution, calculation of Operational Value at Risk (OpVar), testing the model with back testing and comparison of capital adequacy from the results of the calculation of the Basic Indicator Approach (BIA) and the Advance Measurement Approach (AMA). The results of research based on the BIA require an operational risk capital cost of Rp.291,652,000,000. The results of the research on the AMA approach use the frequency of loss distribution parameter for the internal causes of the process with a Geometric distribution of 0.17561, while for the human cause of 0.08511, for the cause of external events amounting to 0.83721. Determination of Frequency of Loss Distribution using Goodness of Fit for internal processes, people and external events. The results of the Operational Value at Risk (OpVar) with a geometric distribution pattern, then the maximum loss that can arise due to human factors is Rp.24,114,480,096, -, for internal process factors of Rp.6,010,929,367, whereas for external causes for Rp. 2,161,092,909. In total operational risk capital needs through the AMA method of Rp. 32,286,502,372.

The underline distribution assumption used in the analysis of share market returns is crucial in risk management. An important aspect related to stock return modelling is to obtain accurate prediction. This paper presents an innovative fitting method called two stages (TS) method for modelling daily stock returns. The proposed approach by first establishing trend in the series, and then separately performing L-moment estimation on the generalized lambda distribution (GLD) parameter. The performance of the TS-GLD models had been evaluated using Monte Carlo simulation and Malaysian Kuala Lumpur Composite Index (KLCI) returns from year 2001 to 2015. Based on k-sample Anderson darling goodness of fit test, the two stages GLD model in location parameter (GLD.1) performed well in all studied cases. The GLD.1 model benefits risk management by providing effective distribution fitting.

Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.

Summary Methods and models for longitudinal data with categorical, multi-dimensional outcomes are quite limited, but they are essential to the study of life histories. For example, in the Swiss Household Panel, information on the co-residence and professional status of several thousand individuals is available through to age 45 years. Interest centres on the time and order of life course events such as having children and working full or part time and the duration of the phases that they delineate. With data of this type, optimal matching and clustering algorithms relying on a distance metric or parametric models of duration in a competing risks framework are used; the appropriateness of each derives from competing goals and orientation. We prefer model-based approaches when certain goals are paramount: simulation of individual trajectories; adjusting for time-dependent covariates; handling multistate trajectories and missing outcomes. Several of these goals are particularly challenging when the number of states is of moderate size, and many transitions are infrequent and/or time inhomogeneous. Using the Swiss Household Panel, we demonstrate the appropriateness of latent class growth curve models for analysing sequence data. In particular, models including heterogeneous dependence structure provide new techniques for assessing goodness of fit as well as yield insights into social processes.

In Pharmacokinetic (PK) and Pharmacodynamic (PD), the hazard rate functions play a central role in modelling time-to-event data. In the context of assessing the appropriateness of a given parametri...

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.

Response time models (RTMs) are of increasing interest in educational and psychological testing. This article focuses on the lognormal model for response times, which is one of the most popular RTMs. Several existing statistics for testing normality and the fit of factor analysis models are repurposed for testing the fit of the lognormal model. A simulation study and two real data examples demonstrate the usefulness of the statistics. The Shapiro–Wilk test of normality and a z-test for factor analysis models were the most powerful in assessing the misfit of the lognormal model.

In this work, we study semiparametric copula models under bivariate censoring. Basing on divergences theory, we propose new estimates for the parameter of the considered model and we establish thei...

In large-scale educational assessments, it is generally required that tests are composed of items that function invariantly across the groups to be compared. Despite efforts to ensure invariance in the item construction phase, for a range of reasons (including the security of items) it is often necessary to account for differential item functioning (DIF) of items post hoc. This typically requires a choice among retaining an item as it is despite its DIF, deleting the item, or resolving (splitting) an item by creating a distinct item for each group. These options involve a trade-off between model fit and the invariance of item parameters, and each option could be valid depending on whether or not the source of DIF is relevant or irrelevant to the variable being assessed. We argue that making a choice requires a careful analysis of statistical DIF and its substantive source. We illustrate our argument by analyzing PISA 2006 science data of three countries (UK, France and Jordan) using the Rasch model, which was the model used for the analyses of all PISA 2006 data. We identify items with real DIF across countries and examine the implications for model fit, invariance, and the validity of cross-country comparisons when these items are either eliminated, resolved or retained.

This study evaluated the performance of probability distribution in various financial periods by investigating the effect of economic cycle on extreme stock return activity. Malaysian stock price KLCI data from 1994–2008 were split into three economy periods correspond to the growth, financial crisis, and the recovery. Four prevalent distributions specifically generalized lambda distribution (GLD), generalized extreme value (GEV), generalized logistic (GLO), and generalized pareto (GPA) were employed to model weekly and monthly maximum and minimum Kuala Lumpur Composite Index (KLCI) share returns. The L-moment approach was used to estimate the parameter while k-sample Anderson darling (k-ad) test was applied to measure the goodness of fit estimation. In conclusion, GLD is the most appropriate distribution representing a weekly maximum minimum return for overall three economic scenarios in Malaysia.

Current insurance models, assuming that inter-arrival time of claims, are distributed randomly and thus well approximated by Poisson processes. Here we provide clear proof that the timing of inter-claims fits by non-Poisson patterns, marked by rapid events, separated by long periods of inactivity. The time of inter-arrival claims will be heavy tailed, most claims will be executed quickly, while a few will have very long waiting times. We will model and analysis of insurance based on claim inter-arrival time, the time interval between two successive claims and the ability to carry out such modeling was limited by a lack of ecologically relevant data collected on claims inter-arrival. We propose a structured process behavior model based on data from Egyptian fire insurance company. Our analysis shows that claim activities can be represented by non-Poisson processes and that the subsequent distribution of inter-arrival activity times follows the Pareto distribution. These results will help researchers understand daily behavioral trends and create more sophisticated predictive models of claims.

Rank-based procedures are commonly used for inference in copula models for continuous responses whose behavior does not depend on covariates. This paper describes how these procedures can be adapted to the broader framework in which (possibly non-linear) regression models for the marginal responses are linked by a copula that does not depend on covariates. The validity of many of these techniques can be derived from the asymptotic equivalence between the classical empirical copula process and its analog based on suitable residuals from the marginal models. Moment-based parameter estimation and copula goodness-of-fit tests are shown to remain valid under weak conditions on the marginal error term distributions, even when the residual-based empirical copula process fails to converge weakly. The performance of these procedures is evaluated through simulation in the context of two general insurance applications: micro-level multivariate insurance claims, and dependent loss triangles.

This paper analyzes credit rating default dependencies in a multisectoral framework. Using Mergent's FISD database, we study the default series in the U.S. over the last two decades, disaggregating defaults by industry-sector group. During this period, two main waves of default occurred: the implosion of the “dot-com” bubble and the global financial crisis. We estimate a Multivariate Autoregressive Conditional Poisson model according to the biweekly number of defaults that occurred in different sectors of the economy from 1996 to 2015. We discuss the contagion effect between sectors in two ways: the degree of transmission of the probability of default from one sector to another, i.e., the “infectivity” of the sector, and the degree of contagion of one sector from another, i.e., the “vulnerability” of the sector. Our results show differences between the sectors' relations during the first and second part of our sample. We add some exogenous variables to the analysis and evaluate their contribution to the goodness of fit.

Background Estimates of future survival can be a key evidence source when deciding if a medical treatment should be funded. Current practice is to use standard parametric models for generating extrapolations. Several emerging, more flexible, survival models are available which can provide improved within-sample fit. This study aimed to assess if these emerging practice models also provided improved extrapolations. Methods Both a simulation study and a case-study were used to assess the goodness of fit of five classes of survival model. These were: current practice models, Royston Parmar models (RPMs), Fractional polynomials (FPs), Generalised additive models (GAMs), and Dynamic survival models (DSMs). The simulation study used a mixture-Weibull model as the data-generating mechanism with varying lengths of follow-up and sample sizes. The case-study was long-term follow-up of a prostate cancer trial. For both studies, models were fit to an early data-cut of the data, and extrapolations compared to the known long-term follow-up. Results The emerging practice models provided better within-sample fit than current practice models. For data-rich simulation scenarios (large sample sizes or long follow-up), the GAMs and DSMs provided improved extrapolations compared with current practice. Extrapolations from FPs were always very poor whilst those from RPMs were similar to current practice. With short follow-up all the models struggled to provide useful extrapolations. In the case-study all the models provided very similar estimates, but extrapolations were all poor as no model was able to capture a turning-point during the extrapolated period. Conclusions Good within-sample fit does not guarantee good extrapolation performance. Both GAMs and DSMs may be considered as candidate extrapolation models in addition to current practice. Further research into when these flexible models are most useful, and the role of external evidence to improve extrapolations is required.

Data privacy is a growing concern in modern data analyses as more and more types of information about individuals are collected and shared. Statistical analysis in consideration of privacy is thus becoming an exciting area of research. Differential privacy can provide a means by which one can measure the stochastic risk of violating the privacy of individuals that can result from conducting an analysis, such as a simple query from a database and a hypothesis test. The main interest of the work is a goodness-of-fit test that compares the sampled data to a known distribution. Many differentially private goodness-of-fit tests have been proposed for discrete random variables, but little work has been done for continuous variables. The objective is to review some existing tests that guarantee differential privacy for discrete random variables, and to propose an extension to continuous cases via a discretization process. The proposed test procedures are demonstrated through simulated examples and applied to the Household Financial Welfare Survey of South Korea in 2018.

We introduce a four-factor arithmetic model for electricity baseload spot prices in Germany and Austria. The model consists of a deterministic seasonality and trend function, both short- and long-term stochastic components, and exogenous factors such as the daily wind energy production forecasts, the residual demand and the wind penetration index. We describe the short-term stochastic factor by a Levy semi-stationary (LSS) process, and the long-term component is modelled as a Levy process with increments belonging to the class of generalised hyperbolic distributions. We derive the corresponding futures prices and develop an inference methodology for our multi-factor model. The methodology allows to infer the various factors in a step-wise procedure taking empirical spot prices, futures prices and wind energy production and total load data into account. Our empirical work shows that taking into account the impact of the wind energy generation on the prices improves the goodness of fit. Moreover, we demonstrate that the class of LSS processes can be used for modelling the exogenous variables including wind energy production, residual demand and the wind penetration index.

Η συγκεκριμένη μελέτη παρουσιάζει τεστ καλής προσαρμογής για το συστηματικό μέρος σε μοντέλα τυχαίων επιδράσεων. Συγκεκριμένα προτείνονται τεστ που βασίζονται στο διαχωρισμό των παρατηρήσεων σε αμοιβαίως αποκλειόμενες ομάδες, όπως και σταθμισμένες εκδοχές τους που βασίζονται στη συσχέτιση μεταξύ μίας κατάλληλα προσαρμοσμένης μεταβλητής υποψήφιας για είσοδο στο μοντέλο και των καταλοίπων του μηδενικού μοντέλου. Επιπρόσθετα, προτείνονται σταθμισμένες εκδοχές των διαδικασιών του σωρευτικού αθροίσματος και του κινητού μέσου. Η διαδικασία εκτίμησης που χρησιμοποιείται βασίζεται στην h-πιθανοφάνεια, οπότε όλες οι ποσότητες που χρησιμοποιούνται για να σχηματιστούν τα παραπάνω στατιστικά προέρχονται από αυτή τη μέθοδο εκτίμησης. Οι προσομοιώσεις που παρουσιάζονται έχουν σχεδιαστεί έτσι ώστε να εξετάσουν την επίδοση των στατιστικών σε διαφορετικά μεγέθη δείγματος, διακύμανσης των τυχαίων επιδράσεων, διάφορες αποκλίσεις από το πραγματικό μοντέλο, κ.λπ. Τα αποτελέσματα υποδεικνύουν μία καλύτερη απόδοση των σταθμισμένων στατιστικών, ενώ ωστόσο, υπάρχουν περιπτώσεις όπου κάποια μη σταθμισμένα τεστ επιδεικνύουν μία ίδιου βαθμού απόδοση με αυτή των σταθμισμένων ανάλογών τους. Όλες οι προτεινόμενες στατιστικές εφαρμόζονται σε δύο πραγματικά σύνολα δεδομένων.

Focus of Presentation Causation is critical if epidemiology is to be relevant to public health. Mendelian Randomisation makes causal inference about from simply observing an association between the outcome and a genetic variable that has been conferred the title of “instrumental” because the proponents consider it satisfies some assumptions perfectly. We take this association as the starting point of a Popperian approach that tries to falsify causal hypotheses by relaxing assumptions and considering alternate models. Findings We developed methods to calculate test-of-fit statistics for different causal scenarios based on the joint changes in regression coefficients, using simulations and bootstrapping methods. Let Y be the outcome, X a putative cause, and G a potential instrumental variable associated with X and Y. We regress Y against X and G alone, and with X and G together. We predicted the changes to regression coefficients that should occur under three scenarios; (i) X causes Y, (ii) there is a factor C associated with Y, X and G. and (ii) Y causes X. We compared goodness-of-fit statistics across scenarios, and for combinations of scenarios (given multiple causal processes might co-exist). We present findings from application to data on body mass index and DNA methylation and compare with Mendelian Randomisation analyses. Conclusions/Implications Robust inference can be made but the sample sizes and strengths of associations need to be substantive. Key messages Causation is a fundamentally important issue that should, and can, be addressed by trying to disprove it, rather than by finding evidence for it.

Statistical mediation analysis is used to investigate mechanisms through which a randomized intervention causally affects an outcome variable. Mediation analysis is often carried out in a pretest-posttest control group design because it is a common choice for evaluating experimental manipulations in the behavioral and social sciences. There are four different two-wave (i.e., pretest-posttest) mediation models that can be estimated using either linear regression or a Latent Change Score (LCS) specification in Structural Equation Modeling: Analysis of Covariance, difference and residualized change scores, and a cross-sectional model. Linear regression modeling and the LCS specification of the two-wave mediation models provide identical mediated effect estimates but the two modeling approaches differ in their assumptions of model fit. Linear regression modeling assumes each of the four two-wave mediation models fit the data perfectly whereas the LCS specification allows researchers to evaluate the model constraints implied by the difference score, residualized change score, and cross-sectional models via model fit indices. Therefore, the purpose of this paper is to provide a conceptual and statistical comparison of two-wave mediation models. Models were compared on the assumptions they make about time-lags and cross-lagged effects as well as statistically using both standard measures of model fit (χ2, RMSEA, and CFI) and newly proposed T-size measures of model fit for the two-wave mediation models. Overall, the LCS specification makes clear the assumptions that are often implicitly made when fitting two-wave mediation models with regression. In a Monte Carlo simulation, the standard model fit indices and newly proposed T-size measures of model fit generally correctly identified the best fitting two-wave mediation model.

Tuna is a renewable resource that has been managed regionally, but its worldwide capacity for regeneration is still little known. A time-series dataset of tuna catches was used to develop nonlinear univariate models for monitoring the sustainability of tuna catches. Two approaches were compared: 1) fitting an ARIMA-spline model to the volume of annual tuna catches and 2) combining neural networks with an ARIMA model to fit the annual changes in volume. These models offer competitive forecasting performance with small percentage errors. By averaging results of the best model developed in each of these approaches, our ensemble forecast predicts that world tuna catches will reach the optimal level of 5.09 million tons in 2025, remain stable thereafter until 2033, and start decreasing about 0.78 % annually. These models could be used by regional fishery management groups to discover discrepancies between such projections and other science-based estimations of the maximum sustainable output. HIGHLIGHTS AnARIMA-spline model is practical for forecasting time series with uncertainties and complex interaction of variables The plausibility of forecasts is essential as the goodness of fit for statistical model validation The ensemble forecasts of results from modelling both catches and the changes of catches offer an alternative view for monitoring trend of fishery practices GRAPHICAL ABSTRACT

Under the Basel II and Basel III agreements, the probability of default (PD) is a key parameter used in calculating expected credit loss (ECL), which is typically defined as: PD × Loss Given Default × Exposure at Default. In practice or in regulatory requirements, gross domestic product (GDP) has been adopted in the PD estimation model. Due to the problem of excessive fluctuation and highly volatile ECL estimation, models that produce satisfactory PD and thus ECL estimations in the context of existing risk management techniques are lacking. In this study, we explore the usage of the credit default swap index (CDX), a market’s expectation of future PD, as a predictor of the default rate (DR). By comparing the goodness-of-fit of logistic regression, several conclusions are drawn. Firstly, in general, GDP has considerable explanatory power for the default rate which is consistent with current models in practice. Secondly, although both GDP and CDX fit the DR well for rating B class, CDX has a significantly better fit of DR for ratings [A, Baa, Ba]. Thirdly, compared with low-rated companies, the relationship between the DR and GDP is relatively weak for rating A. This phenomenon implies that, in addition to using macroeconomic variables and firm-specific explanatory variables in the PD estimation model, high-rated companies exhibit a greater need to use market supplemental information, such as CDX, to capture the changes in the DR.

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.