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Compared with the conditional mean regression-based scalar-onfunction regression model, the scalar-on-function quantile regression is robust to outliers in the response variable. However, it is susceptible to outliers in the functional predictor (called leverage points). This is because the influence function of the regression quantiles is bounded in the response variable but unbounded in the predictor space. The leverage points may alter the eigenstructure of the predictor matrix, leading to poor estimation and prediction results. This study proposes a robust procedure to estimate the model parameters in the scalar-on-function quantile regression method and produce reliable predictions in the presence of both outliers and leverage points. The proposed method is based on a functional partial quantile regression procedure. We propose a weighted partial quantile covariance to obtain functional partial quantile components of the scalar-on-function quantile regression model. After the decomposition, the model parameters are estimated via a weighted loss function, where the robustness is obtained by iteratively reweighting the partial quantile components. The estimation and prediction performance of the proposed method is evaluated by a series of Monte-Carlo experiments and an empirical data example. The results are compared favorably with several existing methods. The method is implemented in an R package robfpqr.

The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in start time and measurement lag. The data were analyzed using precise time metric, where variation in time was accounted for, and then analyzed using coarse time metric indicating only that the assessment took place during the time window. Results indicated that models estimated using the coarse time metric resulted in biased parameter estimates as well as larger standard errors and larger variances and covariances for intercept and slope. In particular, the coupling parameter estimates – which are unique to BLCS models – were biased with larger standard errors. An illustrative example of longitudinal bivariate relations between math and reading achievement in a nationally representative survey of children is then used to demonstrate how results and conclusions differ when using time metrics of varying precision. Implications and future directions are discussed.

Anyone with a smartphone can take pictures anytime anywhere. This opens the opportunity for researchers to collect photos to augment traditional Web survey data. We conducted an experimental survey asking 2700 members of the Dutch LISS panel about their dwelling conditions. Depending on the condition, they were asked to either take several photos of their house or answer a set of survey questions about the same topics. This paper documents the feasibility of collecting pictures instead of answers in a web survey and studies the consequences of using pictures/questions or a choice for components of Total Survey Error. We find that respondents in our study were much more willing to answer survey questions than to take pictures, but this difference depended on the difficulty of the topic. We then compare the quality of the data from the pictures of the heating systems with the survey responses. Here we find that our pictures contain more useful information than the survey responses. Overall, we conclude that asking respondents to take a picture within a survey can be a feasible way to collect high-quality data.

The highfrequency package for the R programming language provides functionality for pre-processing financial high-frequency data, analyzing intraday stock returns, and forecasting stock market volatility. For academics and practitioners alike, it provides a tool chain required to work with such datasets and to conduct statistical analyses dedicated to spot volatility, jumps, realized measures, and many more. We showcase our implemented routines and models on raw high-frequency data from large stock exchanges.

Summary Clinical trials often aim to compare two groups of patients for efficacy and/or toxicity depending on covariates such as dose. Examples include the comparison of populations from different geographic regions or age classes or, alternatively, of different treatment groups. Similarity of these groups can be claimed if the difference in average outcome is below a certain margin over the entire covariate range. In this article, we consider the problem of testing for similarity in the case that efficacy and toxicity are measured as binary outcome variables. We develop a new test for the assessment of similarity of two groups for a single binary endpoint. Our approach is based on estimating the maximal deviation between the curves describing the responses of the two groups, followed by a parametric bootstrap test. Further, using a two-dimensional Gumbel-type model we develop methodology to establish similarity for (correlated) binary efficacy–toxicity outcomes. We investigate the operating characteristics of the proposed methodology by means of a simulation study and present a case study as an illustration.

The manuscript discusses how to incorporate random effects for quantile regression models for clustered data with focus on settings with many but small clusters. The paper has three contributions: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed {by a pseudo likelihood approach (the LQMM method)} and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study., Accepted for Econometrics and Statistics

Inference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory because the chosen model is usually the result of a data-adaptive model selection process, which may induce excess uncertainty that is not usually acknowledged. Moreover, the assumptions encoded in the chosen model rarely represent some a priori known, ground truth, making standard inferences prone to bias, but also failing to give a pure reflection of the information that is contained in the data. Inspired by developments on assumption-free inference for so-called projection parameters, we here propose novel nonparametric definitions of main effect estimands and effect modification estimands. These reduce to standard main effect and effect modification parameters in generalised linear models when these models are correctly specified, but have the advantage that they continue to capture respectively the primary (conditional) association between two variables, or the degree to which two variables interact (in a statistical sense) in their effect on outcome, even when these models are misspecified. We achieve an assumption-lean inference for these estimands (and thus for the underlying regression parameters) by deriving their influence curve under the nonparametric model and invoking flexible data-adaptive (e.g., machine learning) procedures., Comment: 37 pages, 2 tables

With the availability of massive amounts of data from electronic health records and registry databases, incorporating time-varying patient information to improve risk prediction has attracted great attention. To exploit the growing amount of predictor information over time, we develop a unified framework for landmark prediction using survival tree ensembles, where an updated prediction can be performed when new information becomes available. Compared to conventional landmark prediction with fixed landmark times, our methods allow the landmark times to be subject-specific and triggered by an intermediate clinical event. Moreover, the nonparametric approach circumvents the thorny issue of model incompatibility at different landmark times. In our framework, both the longitudinal predictors and the event time outcome are subject to right censoring, and thus existing tree-based approaches cannot be directly applied. To tackle the analytical challenges, we propose a risk-set-based ensemble procedure by averaging martingale estimating equations from individual trees. Extensive simulation studies are conducted to evaluate the performance of our methods. The methods are applied to the Cystic Fibrosis Foundation Patient Registry (CFFPR) data to perform dynamic prediction of lung disease in cystic fibrosis patients and to identify important prognosis factors.

The optimal dynamic treatment rule (ODTR) framework offers an approach for understanding which kinds of patients respond best to specific treatments – in other words, treatment effect heterogeneity. Recently, there has been a proliferation of methods for estimating the ODTR. One such method is an extension of the SuperLearner algorithm – an ensemble method to optimally combine candidate algorithms extensively used in prediction problems – to ODTRs. Following the ``causal roadmap,” we causally and statistically define the ODTR and provide an introduction to estimating it using the ODTR SuperLearner. Additionally, we highlight practical choices when implementing the algorithm, including choice of candidate algorithms, metalearners to combine the candidates, and risk functions to select the best combination of algorithms. Using simulations, we illustrate how estimating the ODTR using this SuperLearner approach can uncover treatment effect heterogeneity more effectively than traditional approaches based on fitting a parametric regression of the outcome on the treatment, covariates and treatment-covariate interactions. We investigate the implications of choices in implementing an ODTR SuperLearner at various sample sizes. Our results show the advantages of: (1) including a combination of both flexible machine learning algorithms and simple parametric estimators in the library of candidate algorithms; (2) using an ensemble metalearner to combine candidates rather than selecting only the best-performing candidate; (3) using the mean outcome under the rule as a risk function. Finally, we apply the ODTR SuperLearner to the ``Interventions” study, an ongoing randomized controlled trial, to identify which justice-involved adults with mental illness benefit most from cognitive behavioral therapy to reduce criminal re-offending.

We propose a Cox proportional hazards model with a change hyperplane to allow the effect of risk factors to differ depending on whether a linear combination of baseline covariates exceeds a threshold. The proposed model is a natural extension of the change-point hazards model. We maximize the partial likelihood function for estimation and suggest an m-out-of-n bootstrapping procedure for inference. We establish the asymptotic distribution of the estimators and show that the estimators for the change hyperplane converge in distribution to an integrated composite Poisson process defined on a multidimensional space. Finally, the numerical performance of the proposed approach is demonstrated using simulation studies and an analysis of the Cardiovascular Health Study.

Predicting large fetuses at birth is of great interest to obstetricians. Using an NICHD Scandinavian Study that collected longitudinal ultrasound examination data during pregnancy, we estimate diagnostic accuracy parameters of estimated fetal weight (EFW) at various times during pregnancy in predicting large-for-gestational-age. We adopt a placement value based Bayesian regression model with random effects to estimate ROC curves. The use of placement values allows us to model covariate effects directly on the ROC curves and the adoption of a Bayesian approach accommodates the a priori constraint that an ROC curve of EFW near delivery should dominate another further away. The proposed methodology is shown to perform better than some alternative approaches in simulations and its application to the Scandinavian Study data suggests that diagnostic accuracy of EFW can improve about 65% from week 17 to 37 of gestation.

Electrocochleography (ECochG) measures electrophysiological inner ear potentials in response to acoustic stimulation. These potentials reflect the state of the inner ear and provide important information about its residual function. For cochlear implant (CI) recipients, we can measure ECochG signals directly within the cochlea using the implant electrode. We are able to perform these recordings during and at any point after implantation. However, the analysis and interpretation of ECochG signals are not trivial. To assist the scientific community, we provide our intracochlear ECochG data set, which consists of 4,924 signals recorded from 46 ears with a cochlear implant. We collected data either immediately after electrode insertion or postoperatively in subjects with residual acoustic hearing. This data descriptor aims to provide the research community access to our comprehensive electrophysiological data set and algorithms. It includes all steps from raw data acquisition to signal processing and objective analysis using Deep Learning. In addition, we collected subject demographic data, hearing thresholds, subjective loudness levels, impedance telemetry, radiographic findings, and classification of ECochG signals.

For cohorts with long-term follow-up, the number of years lost due to a certain disease yields a measure with a simple and appealing interpretation. Recently, an overview of the methodology used for this goal has been published, and two measures have been proposed. In this work, we consider a third option that may be useful in settings in which the other two are inappropriate. In all three measures, the survival of the given dataset is compared to the expected survival in the general population which is calculated using external mortality tables. We thoroughly analyze the differences between the three measures, their assumptions, interpretation, and the corresponding estimators. The first measure is defined in a competing risk setting and assumes an excess hazard compared to the population, while the other two measures also allow estimation for groups that live better than the general population. In this case, the observed survival of the patients is compared to that in the population. The starting point of this comparison depends on whether the entry into the study is a hazard changing event (e.g., disease diagnosis or the age at which the inclusion criteria were met). Focusing on the newly defined life years difference measure, we study the estimation of the variance and consider the possible challenges (e.g., extrapolation) that occur in practice. We illustrate its use with a dataset of French Olympic athletes. Finally, an efficient R implementation has been developed for all three measures which make this work easily available to subsequent users. For cohorts with long-term follow-up, the number of years lost due to a certain disease yields a measure with a simple and appealing interpretation. Recently, an overview of the methodology used for this goal has been published, and two measures have been proposed. In this work, we consider a third option that may be useful in settings in which the other two are inappropriate. In all three measures, the survival of the given dataset is compared to the expected survival in the general population which is calculated using external mortality tables. We thoroughly analyze the differences between the three measures, their assumptions, interpretation, and the corresponding estimators. The first measure is defined in a competing risk setting and assumes an excess hazard compared to the population, while the other two measures also allow estimation for groups that live better than the general population. In this case, the observed survival of the patients is compared to that in the population. The starting point of this comparison depends on whether the entry into the study is a hazard changing event (e.g., disease diagnosis or the age at which the inclusion criteria were met). Focusing on the newly defined life years difference measure, we study the estimation of the variance and consider the possible challenges (e.g., extrapolation) that occur in practice. We illustrate its use with a dataset of French Olympic athletes. Finally, an efficient R implementation has been developed for all three measures which make this work easily available to subsequent users.

Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information about changes in the number of infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches, parametric approaches, and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard-to-interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia., Comment: 43 pages, 6 figures in the main text

Identifying influential nodes in a network is a major issue due to the great deal of applications concerned, such as disease spreading and rumor dynamics. That is why, a plethora of centrality measures has emerged over the years in order to rank nodes according to their topological importance in the network. Local metrics such as degree centrality make use of a very limited information and are easy to compute. Global metrics such as betweenness centrality exploit the information of the whole network structure at the cost of a very high computational complexity. Recent works have shown that combining multiple metrics is a promising strategy to quantify the node's influential ability. Our work is in this line. In this paper, we introduce a multi-attributes centrality measure called M-Centrality that combines the information on the position of the node in the network with the local information on its nearest neighborhood. The position is measured by the K-shell decomposition, and the degree variation in the neighborhood of the node quantifies the influence of the local context. In order to examine the performances of the proposed measure, we conduct experiments on small and large scale real-world networks from the perspectives of transmission dynamics and network connectivity. According to the empirical results, the M-Centrality outperforms its alternatives in identifying both influential spreaders and nodes essential to maintain the network connectivity. In addition, its low computational complexity makes it easily applied to large scale networks.

Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure that the model class used to approximate the true forecasting distribution is expressive enough. Yet, characteristics of the model itself, such as its uncertainty or its feature-outcome relationship are not of lesser importance. This paper proposes Autoregressive Transformation Models (ATMs), a model class inspired by various research directions to unite expressive distributional forecasts using a semi-parametric distribution assumption with an interpretable model specification. We demonstrate the properties of ATMs both theoretically and through empirical evaluation on several simulated and real-world forecasting datasets., Statistics and Computing, 33 (2), ISSN:0960-3174, ISSN:1573-1375

21 pages, 6 figures, 8 tables.-- This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, Testing whether two spatial point processes have the same spatial distribution is an important task that can be addressed from different perspectives. A Kolmogorov-Smirnov test with asymptotic calibration and a Cramer von Mises type test with bootstrap calibration have recently been developed to compare the first-order intensity of two observed patterns. Motivated by common practice in epidemiological studies, we introduce a regression test based on the relative risk function with two alternative bootstrap calibrations. This paper compares the performance of these nonparametric tests through both an intensive simulation study, and the application to wildfire and crime data. The three tests provide good calibrations of the null hypothesis for simulated Poisson and non-Poisson spatial point processes, but the Cramer von Mises and regression tests outperform the cost-efficient Kolmogorov-Smirnov test in terms of power. In the real data analysis we have seen that the Kolmogorov-Smirnov test does not detect differences between spatial point patterns when dealing with sparse data. In view of these results, it would be preferable using the Cramer von Mises or regression tests despite their higher computational demand, This work has been supported by Projects MTM2016-78917-R and MTM2016-76969-P (AEI/FEDER, UE), grant AICO/2019/198 from Generalitat Valenciana, and IAP network StUDyS grant 3E120297 from the Belgian Science Policy

The R package abn is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production.

In this paper, we make use of meta-elliptical copula functions to build a new multivariate distribution with fixed marginal distributions and dependence structure to analyze bounded data. Specifica...

Assessing the sizes of active contrasts in un-replicated factorial and fractional factorial experiments by quick and powerful methods are required in analyzing the big data in various research areas of Human endeavors. One of the old methods based on Lenth (1989) is being used in some statistical and data analytical applications which is fast and less efficient. We propose a new class of tests which are simpler, faster, and more powerful using the location median-function ($${\psi }_{med}\left({\varvec{x}}\right)$$ ψ med x ) after being skipped one and/or two times. An empirical study of simulation experiments to compute the critical values, sizes and powers using various sample sizes demonstrate the superiority of our methods. The proposed methods are illustrated in examples which can be employed in various fields of research in conducting data analytics using high computing power and machine learning.

The distribution of the ratio of two independently distributed Lindley random variables $$X$$ X and $$Y$$ Y , with different parameters, is derived. The associated distributional properties are provided. Furthermore, the proposed ratio distribution is fitted to two applications data (COVID-19 and Bladder Cancer Data), and compared it with some well-known right-skewed variations of Lindley distribution, namely; Lindley distribution, new generalized Lindley distribution, new quasi Lindley distribution and a three parameter Lindley distribution. The numerical result of the study reveals that the proposed distribution of two independent Lindley random variables fits better to the above said data sets than the compared distribution.

Expectiles define the only law-invariant, coherent and elicitable risk measure apart from the expectation. The popularity of expectile-based risk measures is steadily growing and their properties have been studied for independent data, but further results are needed to establish that extreme expectiles can be applied with the kind of dependent time series models relevant to finance. In this article we provide a basis for inference on extreme expectiles and expectile-based marginal expected shortfall in a general β-mixing context that encompasses ARMA and GARCH models with heavy-tailed innovations. Our methods allow the estimation of marginal (pertaining to the stationary distribution) and dynamic (conditional on the past) extreme expectile-based risk measures. Simulations and applications to financial returns show that the new estimators and confidence intervals greatly improve on existing ones when the data are dependent.

Spatial statistics is concerned with the analysis of data that have spatial locations associated with them, and those locations are used to model statistical dependence between the data. The spatial data are treated as a single realisation from a probability model that encodes the dependence through both fixed effects and random effects, where randomness is manifest in the underlying spatial process and in the noisy, incomplete, measurement process. The focus of this review article is on the use of basis functions to provide an extremely flexible and computationally efficient way to model spatial processes that are possibly highly non-stationary. Several examples of basis-function models are provided to illustrate how they are used in Gaussian, non-Gaussian, multivariate, and spatio-temporal settings, with applications in geophysics. Our aim is to emphasise the versatility of these spatial statistical models and to demonstrate that they are now centre-stage in a number of application domains. The review concludes with a discussion and illustration of software currently available to fit spatial-basis-function models and implement spatial-statistical prediction., 30 pages, 6 figures

This paper establishes asymptotic results for the maximum likelihood and restricted maximum likelihood (REML) estimators of the parameters in the nested error regression model for clustered data when both of the number of independent clusters and the cluster sizes (the number of observations in each cluster) go to infinity. Under very mild conditions, the estimators are shown to be asymptotically normal with an elegantly structured covariance matrix. There are no restrictions on the rate at which the cluster size tends to infinity but it turns out that we need to treat within cluster parameters (i.e. coefficients of unit-level covariates that vary within clusters and the within cluster variance) differently from between cluster parameters (i.e. coefficients of cluster-level covariates that are constant within clusters and the between cluster variance) because they require different normalisations and are asymptotically independent.

In this paper, we provide a general result under some high level assumptions that shows how to account for the parameter uncertainty problem in test statistics formulated with the quasi maximum likelihood (QML) estimator. We use our general result to develop various test statistics for testing skewness, kurtosis and normality for time series data. We show that the asymptotic distributions of our test statistics coincide with the asymptotic distributions of some tests suggested in the literature. Thus, our general result provides a unified approach for test statistics formulated with the QML estimator for time series data.

Acceptance sampling plan paid the attention of many researchers in the last few years, their works focused basically on Bayesian sampling plans under one-sided decision function and different forms of censoring. In the present paper, a single variable sampling plan for exponential distribution based on type II censored samples under random decision function is developed. For a polynomial loss function, an explicit expression for the Bayes risk is determined. To obtain an approximation for the optimal sampling plan, a simple algorithm based on a discretization method is presented. Finally, an illustrative example and a simulation study followed by extensive tables for the proposed sampling plan are provided.

The polynomial combination (PC) method, proposed by Vink and Van Buuren, is a hot-deck multiple imputation method for imputation models containing squared terms. The method yields unbiased regression estimates and preserves the quadratic relationships in the imputed data for both MCAR and MAR mechanisms. However, Vink and Van Buuren never studied the coverage rate of the PC method. This paper investigates the coverage of the nominal 95% confidence intervals for the polynomial combination method and improves the algorithm to avoid the perfect prediction issue. We also compare the original and the improved PC method to the substantive model compatible fully conditional specification method proposed by Bartlett et al. and elucidate the two imputation methods’ characters.

Pearson’s correlation coefficient is considered a measure of linear association between bivariate random variables X and Y. It is recommended not to use it for other forms of associations. Indeed, for nonlinear monotonic associations alternative measures like Spearman’s rank and Kendall’s tau correlation coefficients are considered more appropriate. These views or opinions on the estimation of association are strongly rooted in the statistical and other empirical sciences. After defining linear and monotonic associations, we will demonstrate that these opinions are incorrect. Pearson’s correlation coefficient should not be ruled out a priori for measuring nonlinear monotonic associations. We will provide examples of practically relevant families of bivariate distribution functions with nonlinear monotonic associations for which Pearson’s correlation is preferred over Spearman’s rank and Kendall’s tau correlation in testing the dependency between X and Y. Alternatively, we will provide a family of bivariate distributions with a linear association between X and Y for which Spearman’s rank and Kendall’s tau are preferred over Pearson’s correlation. Our examples show that existing views on linear and monotonic associations are myths.

This paper analyzes the impact of the COVID-19 crisis on household income in Austria, using detailed administrative labor market data, in combination with micro-simulation techniques that enable specific labor market transitions to be modeled. We find that discretionary fiscal policy measures in Austria are key to counteracting the inequality- and poverty-enhancing effect of COVID-19. Additionally, we find that females tend to experience a greater loss in terms of market income. The Austrian tax–benefit system, however, reduces this gender differences. Disposable income has dropped by around 1% for both males and females. By comparison, males profit mainly from short-time work scheme, while females profit especially from other discretionary policy measures, such as the one-off payment for children.

We study the interplay of non-pharmaceutical containment measures, human behavior, and the spread of COVID-19 in Switzerland. First, we collect sub-national data and construct indices that capture the stringency of containment measures at the cantonal level. Second, we use a vector autoregressive model to analyze feedback effects between our variables of interest via structural impulse responses. Our results suggest that increases in the stringency of containment measures lead to a significant reduction in weekly infections as well as debit card transactions, which serve as a proxy for behavioral changes in the population. Furthermore, analyzing different policy measures individually shows that business closures, recommendations to work from home, and restrictions on gatherings have been particularly effective in containing the spread of COVID-19 in Switzerland. Finally, our findings indicate a sizeable voluntary reduction in debit card transactions in response to a positive infection shock., Swiss Journal of Economics and Statistics, 158 (1), ISSN:0303-9692, ISSN:2235-6282

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (e.g., due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. The trimmed means (TM) estimator operates by setting missing values to the most extreme value, and then “trimming” away equal fractions of both treatment groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the “strong MNAR” and “location shift” assumptions. In this article, we derive formulae for the bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA, MI and TM estimates under a range of plausible data scenarios, to inform sensitivity analyses. The TM approach is illustrated with simulations and in a sensitivity analysis of the CoBalT RCT of cognitive behavioural therapy (CBT) in 469 individuals with 46 months follow-up. Results were consistent with a beneficial CBT treatment effect. The MI estimates are closer to the null than the CCA estimate, whereas the TM estimate was further from the null. We propose using the TM estimator as a sensitivity analysis for data where it is suspected that extreme outcome values are missing.

This paper investigates two test statistics for structural changes and thresh- olds in predictive regression models. The generalized likelihood ratio (GLR) test is proposed for the stationary predictor and the generalized F test is suggested for the unit root predictor. Under the null hypothesis of no structural change and threshold, it is shown that the GLR test statistic converges to a function of a centered Gaussian process, and the generalized F test statistic converges to a function of Brownian motions. A Bootstrap method is proposed to obtain the critical values of test statistics. Simulation studies and a real example are given to assess the performances of the tests.

The Laplace distribution is one of the earliest distributions in probability theory and is a frequently used distribution in many fields. Consequently, various goodness-of-fit tests for the Laplace distribution have been thoroughly derived in theliterature. The purpose of this paper is to carry out a comparative study of these tests as well as a new one we develop. Power comparisons of all such tests are performed via Monte Carlo simulations of sample data generatedfrom twenty seven alternatives distributions. Despite the fact that no single test was found to be most powerful in all situations, several useful recommendations however are made.

To study the heterogeneous nature of lifetimes of certain mechanical or engineering processes, a mixture model of some suitable lifetime distributions may be more appropriate and appealing than simpler models. In this paper, a new mixture family of the lifetime distributions is introduced via harmonic weighted mean of an underlying distribution and the distribution of the proportional hazard model corresponding to the baseline model.The proposed class of distributions includes the general Marshall-Olkin family of distributions as a special case. Some important properties of the proposed model such as survival function, hazard function, order statistics and some results on stochastic ordering are obtained in a general setting. A special case of this new family is considered by employingWeibull distribution as the parent distribution. We derive several properties of the special distribution such as moments,hazard function survival regression and certain characterizations results. Moreover, we estimate the parameters of the model by using frequentist and Bayesian approaches. For Bayesian analysis, five loss functions, namely the squared error loss function (SELF), weighted squared error loss function (WSELF), modified squared error loss function (MSELF), precautionary loss function (PLF), and K-loss function (KLF) are considered. The beta prior as well as the gamma prior are used to obtain the Bayes estimators and posterior risk of the unknown parameters of the model. Furthermore, credible intervals (CIs) and highest posterior density (HPD) intervals are also obtained. A simulation study is presented via Monte Carlo to investigate the bias and mean square error of the maximum likelihood estimators. For illustrative purposes, two real-life applications of the proposed distribution to Kidney and cancer patients are provided.

In this work, the Exponentiated Chen-G family of distributions is studied by generalizing the Chen-G family of distributions through the introduction of an additional shape parameter. The mixture properties of the derived family are studied. Some statistical properties of the family were considered, including moments, entropies, moment generating function, order statistics, quantile function. The estimation of the parameters of the family of distributions was done using the maximum likelihood estimation method, considering complete and censored situations. Using the Exponential distribution as a baseline, the Exponentiated Chen Exponential distribution was obtained and its statistical properties were studied. The Exponentiated Chen Exponential distribution has the Exponentiated Exponential, Exponential, Chen Exponential distributions as submodels. Lastly, the Exponentiated Chen Exponential distribution was applied to two real data sets and the results were compared with its submodels and relative distributions.

In this paper, the applications of the half logistic-Marshall Olkin X family of distributions are investigated with special emphasis to the half logistic-Marshall Olkin Lomax distribution. The specific areas we concentrated are time series modeling, acceptance sampling plan and stress-strength analysis. Different autoregressive minification structures of order one are introduced. The acceptance sampling plan is detailed by considering life time of products following the half logistic-Marshall Olkin Lomax distribution. The stress-strength reliability of the half logistic-Marshall Olkin Lomax distribution is derived and estimated. A simulation study is carried out to examine the bias, mean square error, average confidence length and coverage probability of the maximum likelihood estimator of thestress-strength reliability. Finally a real-life data analysis has also been presented.

New formulas for the moments about zero of the Non-central Chi-Squared and the Non-central Beta distributions are achieved by means of novel approaches. The mixture representation of the former model and a new expansion of the ascending factorial of a binomial are the main ingredients of the first approach, whereas the second one hinges on an interesting relationship of conditional independence and a simple conditional density of the latter model. Then, a simulation study is carried out in order to pursue a twofold purpose: providing numerical validations of the derived moment formulas on one side and discussing the advantages of the new formulas over the existing ones on the other., accepted for publication at Austrian Journal of Statistics on 06 May 2021

This study proposes a new marginal asymmetry model which can infer the relation between marginal ridits of row and column variables for ordinal square contingency tables. When the marginal homogeneity model does not hold, we will apply marginal asymmetry models (e.g., the marginal cumulative logistic and extended marginal homogeneity models). On the other hand, we may measure the degree of departure from the marginal homogeneity model. To measure the degree of that, multiple indexes were proposed. Some of them correspond to the marginal cumulative logistic and extended marginal homogeneity models. The proposed model corresponds to the index, which represents the degree of departure from the MH model, using marginal ridits. We compare the proposed model with the existing marginal asymmetry models and show that the proposed model provides better fit performance than them for real data.

We investigate the parameter estimation of regression models with fixed group effects, when the group variable is missing while group related variables are available. This problem involves clustering to infer the missing group variable based on the group related variables, and regression to build a model on the target variable given the group and eventually additional variables. Thus, this problem can be formulated as the joint distribution modeling of the target and of the group related variables. The usual parameter estimation strategy for this joint model is a two-step approach starting by learning the group variable (clustering step) and then plugging in its estimator for fitting the regression model (regression step). However, this approach is suboptimal (providing in particular biased regression estimates) since it does not make use of the target variable for clustering. Thus, we claim for a simultaneous estimation approach of both clustering and regression, in a semi-parametric framework. Numerical experiments illustrate the benefits of our proposition by considering wide ranges of distributions and regression models. The relevance of our new method is illustrated on real data dealing with problems associated with high blood pressure prevention.

Record linkage aims to bring records together from two or more files that belong to the same statistical entity. Naively treating a linked file as if there are no linkage errors may lead to biased inference. We present two general approaches for compensating for linkage error when calculating and analysing a two-way contingency table for categorical data, and study the following question: under what conditions can a compensation approach improve on the naive approach, where linkage error is not compensated for? To this end, we compare estimation errors, bias, variance and mean square error for the naive approach and two compensation approaches by means of an analytical study as well as a simulation study.

New Zealand police has long been suspected of systematic bias against the indigenous Māori. One resource available to investigate this possibility is the annual counts of police apprehensions and prosecutions, by offence type. However, model specification/fitting is complicated as these data are constrained counts, interdependent and multivariate. For example, there are limited options for factor models beyond continuous or binary data. This is a serious limitation for in our dataset, while measurements are clustered, different individuals are measured at each variable. Focusing on principal component/factor analysis representations, we show that under the canonical logit link, latent variable models can be fitted via Gibbs sampling, to multivariate binomial data of arbitrary trial size by applying Pólya-gamma augmentation to the binomial likelihood. We demonstrate that this modelling approach, by incorporating shrinkage, will produce a fit with lower mean square error than techniques based on deviance minimization commonly employed for binary datasets. By exploring theoretical properties of the proposed models, we demonstrate a larger range of latent structures can be estimated and the presence of hidden replication improves prediction when data are multivariate binomial, which gives us greater flexibility for investigating associations between ethnicity and prosecution probability.

This article examines the spatially varying effect of age on single-family house (SFH) prices. Age has been shown to be a key driver for house depreciation and is usually associated with a negative price effect. In practice, however, there exist deviations from this behavior which are referred to as vintage effects. We estimate a spatially varying coefficients (SVC) model to investigate the spatial structures of vintage effects on SFH pricing. For SFHs in the Canton of Zurich, Switzerland, we find substantial spatial variation in the age effect. In particular, we find a local, strong vintage effect primarily in urban areas compared to pure depreciative age effects in rural locations. Using cross validation, we assess the potential improvement in predictive performance by incorporating spatially varying vintage effects in hedonic models. We find a substantial improvement in out-of-sample predictive performance of SVC models over classical spatial hedonic models.

The robustness of the t linear mixed model (tLMM) has been proved and exploited in many applications. Various publications emerged with the aim of proving superiority with respect to traditional linear mixed models, extending to more general settings and proposing more efficient estimation methods. However, little attention has been paid to the mathematical properties of the model itself and to the evaluation of the proposed estimation methods. In this paper we perform an in-depth analysis of the tLMM, evaluating a direct maximum likelihood estimation method via an intensive simulation study and investigating some identifiability properties. The theoretical findings are illustrated through an application to a dataset collected from a sleep trial.

We study the associations between daily mortality and short-term variations in the ambient concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in four cities in Canada. First, a novel multivariate time series model within Bayesian framework is proposed for exposure assessment, where the response is a mixture of Gamma and Half-Cauchy distributions and the correlations between pollutants vary seasonally. A case-crossover design and conditional logistic regression model is used to relate exposure to disease data for each city, which then are combined to obtain a global estimate of exposure health effects allowing exposure uncertainty. The results suggest that every 10 ppb increase in O3 is associated with a 3.88% (95% credible interval [CI], 2.5%, 5.18%) increase in all-cause mortality, a 5.04% (2.84%, 7.43%) increase in circulatory mortality, a 7.87% (2.4%, 12.9%) increase in respiratory mortality, a 0.76% (0.19%, 1.35%) increase in all-cause morbidity and a 6.6% (0.58%, 12.7%) increase in respiratory morbidity. Similarly, every 10 ppb increase in NO2 is associated with a 2.13% (0.42%, 3.87%) increase in circulatory morbidity. The health impacts of PM2.5 are not found to be present once other pollutants are accounted for.

A novel method for testing the null hypothesis of no effect of a covariate on the response is proposed in functional linear concurrent regression. An equivalent random effects formulation of the functional regression model is established under which the testing problem reduces to testing for zero variance component for random effects. For this purpose, a one-sided score test approach is used, which is an extension of the classical score test. Theoretical justification is provided as to why the proposed testing procedure has the correct levels (asymptotically) under null using standard assumptions. Using numerical simulations, the testing method is shown to have the desired type I error rate and higher power compared to a bootstrapped F test currently existing in the literature. The model and the testing procedure give good performances even when the data are sparsely observed, and the functional covariate is contaminated with noise. Applications of the proposed testing method are demonstrated on gait data and a study of child mortality.

This paper empirically examines the economic effects of COVID-19 vaccine rollouts using a cross-country daily database of vaccinations and high-frequency indicators of economic activity—nitrogen dioxide (NO2) emissions, carbon monoxide (CO) emissions, and Google mobility indices—for a sample of 46 countries over the period December 16, 2020 to June 20, 2021. Using surprises in vaccines administered, we find that an unexpected increase in vaccination per capita is associated with a significant increase in economic activity. We also find evidence for nonlinear effects of vaccines, with the marginal economic benefits being larger when vaccination rates are higher. Country-specific conditions play an important role, with lower economic gains if strict containment measures are in place or if the country is experiencing a severe outbreak. Finally, the results provide evidence of spillovers across borders, highlighting the importance of equitable access to vaccines across nations.

In meta analysis, a diverse range of methods for combining multiple p-values have been applied throughout the scientific literature. For sparse signals where only a small proportion of the p-values are truly significant, a technique called higher criticism has previously been shown to have asymptotic consistency and more power than Fisher’s original method. However, higher criticism and other related methods can still lack power. Three new, simple to compute statistics are now proposed for detecting sparse signals, based on standardizing partial sums or products of p-value order statistics. The use of standardization is theoretically justified with results demonstrating asymptotic normality, and avoids the computational difficulties encountered when working with analytic forms of the distributions of the partial sums and products. In particular, the standardized partial product demonstrates more power than existing methods for both the standard Gaussian mixture model and a real data example from computer network modeling.