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Optimal designs for homoscedastic functional polynomial measurement error models AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-04-12 Min-Jue Zhang, Rong-Xian Yue
This paper considers the construction of optimal designs for homoscedastic functional polynomial measurement error models. The general equivalence theorems are given to check the optimality of a given design, based on the locally and Bayesian D-optimality criteria. The explicit characterizations of the locally and Bayesian D-optimal designs are provided. The results are illustrated by numerical analysis
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Local influence analysis for GMM estimation AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-04-08 Jun Lu, Wen Gan, Lei Shi
The generalized method of moments (GMM) is an important estimation procedure in many areas of economics and finance, and it is well known that this estimation is highly sensitive to the presence of outliers and influential observations. Case-deletion diagnostic has been studied in GMM estimation; however, it is surprised that local influence analysis is under explored. To this end, a local influence
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A copula-based multivariate hidden Markov model for modelling momentum in football AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-03-19 Marius Ötting, Roland Langrock, Antonello Maruotti
We investigate the potential occurrence of change points—commonly referred to as “momentum shifts”—in the dynamics of football matches. For that purpose, we model minute-by-minute in-game statistics of Bundesliga matches using hidden Markov models (HMMs). To allow for within-state dependence of the variables, we formulate multivariate state-dependent distributions using copulas. For the Bundesliga
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Is EM really necessary here? Examples where it seems simpler not to use EM AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-03-18 Iain L. MacDonald
If one is to judge by counts of citations of the fundamental paper (Dempster in JRSSB 39: 1–38, 1977), EM algorithms are a runaway success. But it is surprisingly easy to find published applications of EM that are unnecessary, in the sense that there are simpler methods available that will solve the relevant estimation problems. In particular, such problems can often be solved by the simple expedient
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A variable selection procedure for depth measures AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-03-13 Agustín Alvarez, Marcela Svarc
We herein introduce variable selection procedures based on depth similarity, aimed at identifying a small subset of variables that can better explain the depth assigned to each point in space. Our study is not intended to deal with the case of high-dimensional data. Identifying noisy and dependent variables helps us understand the underlying distribution of a given dataset. The asymptotic behaviour
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Positive-definite modification of a covariance matrix by minimizing the matrix $$\ell_{\infty}$$ ℓ ∞ norm with applications to portfolio optimization AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-03-13 Seonghun Cho, Shota Katayama, Johan Lim, Young-Geun Choi
The covariance matrix, which should be estimated from the data, plays an important role in many multivariate procedures, and its positive definiteness (PDness) is essential for the validity of the procedures. Recently, many regularized estimators have been proposed and shown to be consistent in estimating the true matrix and its support under various structural assumptions on the true covariance matrix
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A smooth dynamic network model for patent collaboration data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-03-11 Verena Bauer, Dietmar Harhoff, Göran Kauermann
The development and application of models, which take the evolution of network dynamics into account, are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the collaboration of inventors using EU patent data. As event we consider the submission of a joint patent and
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A harmonically weighted filter for cyclical long memory processes AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-03-08 Federico Maddanu
The estimation of the long memory parameter d is a widely discussed issue in the literature. The harmonically weighted (HW) process was recently introduced for long memory time series with an unbounded spectral density at the origin. In contrast to the most famous fractionally integrated process, the HW approach does not require the estimation of the d parameter, but it may be just as able to capture
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Predicting the popularity of tweets using internal and external knowledge: an empirical Bayes type approach AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-02-26 Wai Hong Tan, Feng Chen
The problem of tweet popularity prediction, or forecasting the total number of retweets stemming from an ancestral tweet, has attracted considerable interest recently. The prediction can be accomplished by fitting a point process model to the sequence of retweet times up to a certain censoring time and project the fitted model to a future time point. However, models employing such approach tend to
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Unified approach for regression models with nonmonotone missing at random data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-01-28 Yang Zhao, Meng Liu
Unified approach (Chen and Chen in J R Stat Soc B 62(3):449–460, 2000) uses a working regression model to extract information from auxiliary variables in two-stage study for computing an efficient estimator of regression parameter. As far as we know, the method is limited to deal with missing complete at random data in a simple monotone missing data pattern. In this research, we extend the unified
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Optimal classification scores based on multivariate marker transformations AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2021-01-03 Pablo Martínez-Camblor, Sonia Pérez-Fernández, Susana Díaz-Coto
Modern science frequently involves the study of complex relationships among effects and factors. Flexible statistical tools are commonly used to visualize nonlinear associations. When our interest is to study the discrimination capacity of a multivariate marker on a binary outcome, the theoretical transformation leading to the optimal results in terms of sensitivity and specificity has already been
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Cyber risk ordering with rank-based statistical models AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-12-09 Paolo Giudici, Emanuela Raffinetti
In a world that is increasingly connected on-line, cyber risks become critical. Cyber risk management is very difficult, as cyber loss data are typically not disclosed. To mitigate the reputational risks associated with their disclosure, loss data may be collected in terms of ordered severity levels. However, to date, there are no risk models for ordinal cyber data. We fill the gap, proposing a rank-based
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Additive models for extremal quantile regression with Pareto-type distributions AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-11-23 Takuma Yoshida
Estimating conditional quantiles in the tail of a distribution is an important problem for several applications. However, data sparsity indicates that the predictions of tail behavior are more difficult compared with those for the mean or center quantiles, in particular, when a multivariate covariate is used. As additive models are known to be an efficient approach for multiple regression, this study
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Visualizing the decision rules behind the ROC curves: understanding the classification process AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-11-13 Sonia Pérez-Fernández, Pablo Martínez-Camblor, Peter Filzmoser, Norberto Corral
The receiver operating characteristic (ROC) curve is a graphical method commonly used to study the capacity of continuous variables (markers) to properly classify subjects into one of two groups. The decision made is ultimately endorsed by a classification subset on the space where the marker is defined. In this paper, we study graphical representations and propose visual forms to reflect those classification
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Heteroscedastic nonlinear regression models using asymmetric and heavy tailed two-piece distributions AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-11-05 Akram Hoseinzadeh, Mohsen Maleki, Zahra Khodadadi
In this paper, heteroscedastic nonlinear regression (HNLR) models under the flexible class of two–piece distributions based on the scale mixtures of normal (TP–SMN) family were examined. This novel class of nonlinear regression (NLR) models is a generalization of the well-known heteroscedastic symmetrical nonlinear regression models. The TP–SMN is a rich class of distributions that covers symmetric
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Confidence regions and other tools for an extension of correspondence analysis based on cumulative frequencies AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-10-26 Antonello D’Ambra, Pietro Amenta, Eric J. Beh
Over the past 50 years, correspondence analysis (CA) has increasingly been used by data analysts to examine the association structure of categorical variables that are cross-classified to form a contingency table. However, the literature has paid little attention to the case where the variables are ordinal. Indeed, Pearson’s chi-squared statistic \(X^{2}\) can perform badly in studying the association
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OLS Estimation of Markov switching VAR models: asymptotics and application to energy use AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-10-22 Maddalena Cavicchioli
We show that the ordinary least squares (OLS) estimates of population parameters for Markov switching vector autoregressive (MS VAR) models coincide with the maximum likelihood estimates. Then, we propose an algorithm in matrix form for the estimation of model parameters, and derive an explicit expression in closed-form for the asymptotic covariance matrix of the OLS estimator of such models. The obtained
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A new mixed first-order integer-valued autoregressive process with Poisson innovations AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-10-12 Daniel L. R. Orozco, Lucas O. F. Sales, Luz M. Z. Fernández, André L. S. Pinho
Integer-valued time series, seen as a collection of observations measured sequentially over time, have been studied with deep notoriety in recent years, with applications and new proposals of autoregressive models that broaden the field of study. This work proposes a new mixed integer-valued first-order autoregressive model with Poisson innovations, denoted POMINAR(1), mixing two operators known as
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Emergence of the wrapped Cauchy distribution in mixed directional data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-10-09 Joseph D. Bailey, Edward A. Codling
Inferring the most appropriate distribution (or distributions) to describe observed directional data is important in many applications of circular statistics. In particular, animal movement paths are typically analysed and modelled by considering the distribution of step lengths and turning (or absolute) angles. Here we demonstrate that a single-wrapped Cauchy distribution can appear to fit directional
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Random coefficients integer-valued threshold autoregressive processes driven by logistic regression AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-10-07 Kai Yang, Han Li, Dehui Wang, Chenhui Zhang
In this article, we introduce a new random coefficients self-exciting threshold integer-valued autoregressive process. The autoregressive coefficients are driven by a logistic regression structure, so that the explanatory variables can be included. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares and conditional maximum likelihood estimators, as
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The exact equivalence of distance and kernel methods in hypothesis testing AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-09-30 Cencheng Shen, Joshua T. Vogelstein
Distance correlation and Hilbert-Schmidt independence criterion are widely used for independence testing, two-sample testing, and many inference tasks in statistics and machine learning. These two methods are tightly related, yet are treated as two different entities in the majority of existing literature. In this paper, we propose a simple and elegant bijection between metric and kernel. The bijective
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Improved testing inferences for beta regressions with parametric mean link function AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-08-28 Cristine Rauber, Francisco Cribari-Neto, Fábio M. Bayer
Beta regressions are widely used for modeling random variables that assume values in the standard unit interval, (0, 1), such as rates, proportions, and income concentration indices. Parameter estimation is typically performed via maximum likelihood, and hypothesis testing inferences on the model parameters are commonly performed using the likelihood ratio test. Such a test, however, may deliver inaccurate
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Structural equation modeling with time dependence: an application comparing Brazilian energy distributors AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-08-18 Vinícius Diniz Mayrink, Renato Valladares Panaro, Marcelo Azevedo Costa
This study proposes a Bayesian structural equation model (SEM) to explore financial and economic sustainability indicators, considered by the Brazilian energy regulator (ANEEL), to evaluate the performance of energy distribution companies. The methodology applies confirmatory factor analysis for dimension reduction of the original multivariate data set into few representative latent variables (factors)
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On Poisson-exponential-Tweedie models for ultra-overdispersed count data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-08-11 Rahma Abid, Célestin C. Kokonendji, Afif Masmoudi
We introduce a new class of Poisson-exponential-Tweedie (PET) mixture in the framework of generalized linear models for ultra-overdispersed count data. The mean–variance relationship is of the form \(m+m^{2}+\phi m^{p}\), where \(\phi\) and p are the dispersion and Tweedie power parameters, respectively. The proposed model is equivalent to the exponential-Poisson–Tweedie models arising from geometric
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Empirical likelihood inference for rank regression with doubly truncated data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-07-27 Xiaohui Yuan, Huixian Li, Tianqing Liu
For regression analysis of doubly truncated data, we propose two empirical likelihood (EL) inference approaches, called non-smooth EL and non-smooth Jackknife EL (JEL), to make inference about regression parameters based on the generalized estimating equations of existing weighted rank estimators. The limiting distributions of non-smooth log-EL and log-JEL ratios statistics are derived and non-smooth
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Bayesian analysis of multivariate ordered probit model with individual heterogeneity AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-06-23 Lei Shi
In recent years, models incorporating heterogeneity among individuals have become increasingly popular in the analyses on subjective ordered choice data. However, there are rare previous studies that include individual heterogeneity in the multivariate ordered probit model. In this article, we describe the Bayesian multivariate ordered probit model introduced by Chen and Dey (in: Dey, Ghosh, Mallick
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A non-homogeneous Poisson process geostatistical model with spatial deformation AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-06-18 Fidel Ernesto Castro Morales, Lorena Vicini
In this paper, we propose a geostatistical model for the counting process using a non-homogeneous Poisson model. This work aims to model the intensity function as the sum of two components: spatial and temporal. The spatial component is modeled using a Gaussian process in which the covariance structure is assumed to be anisotropic. Anisotropy is incorporated by applying a spatial deformation approach
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Variance formulas for estimated mean response and predicted response with external intervention based on the back-door criterion in linear structural equation models AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-06-15 Manabu Kuroki, Hisayoshi Nanmo
This paper considers a situation in which cause–effect relationships among variables can be described by a linear structural equation model (linear SEM) and the corresponding directed acyclic graph (DAG). By considering a set of covariates that satisfies the back-door criterion, we formulate (1) the variances of the estimated mean response and (2) the mean squared error (MSE) of the predicted response
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Count outcome meta-analysis for comparing treatments by fusing mixed data sources: comparing interventions using across report information AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-06-11 Dankmar Böhning, Patarawan Sangnawakij
Assessing interventions applied to target populations is a matter of prime interest. Studies are usually undertaken to see whether an alternative intervention is superior (or at least equivalent) to a comparable standard intervention. This is typically achieved by comparing alternative and standard intervention within a given study, and the developed meta-analytic methodology is building on this assumption
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Biomarker assessment in ROC curve analysis using the length of the curve as an index of diagnostic accuracy: the binormal model framework AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-06-10 Alba M. Franco-Pereira, Christos T. Nakas, M. Carmen Pardo
In receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) is undoubtedly the most widely used index of diagnostic accuracy for the assessment of the utility of a biomarker or for the comparison of competing biomarkers. Along with the AUC, the maximum of the Youden index, J, is often used both as an index of diagnostic accuracy and as a tool useful for the estimation
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Efficient estimation of cumulative distribution function using moving extreme ranked set sampling with application to reliability AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-06-06 Ehsan Zamanzade, M. Mahdizadeh, Hani M. Samawi
In this article, we consider the problem of estimating cumulative distribution function (CDF) and a reliability parameter using moving extreme ranked set sampling (MERSS). Two different CDF estimators are described and compared with their competitors in simple random sampling (SRS) and ranked set sampling (RSS). It turns out the CDF estimators in MERSS can be more efficient than their competitors in
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Goodness of fit test for general linear model with nonignorable missing on response variable AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-05-05 Fayyaz Bahari, Safar Parsi, Mojtaba Ganjali
In this paper, we consider a general linear model where missing data occur in the response variable with a nonignorable mechanism. Also, to deal with missing data, we assume that the probability of missing data follows a logistic model. The main purpose of this paper is to construct some test functions to check the goodness of fit of the general linear model based on the score-type test. To achieve
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Penalized empirical likelihood for partially linear errors-in-variables models AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-03-07 Xia Chen, Liyue Mao
In this paper, we study penalized empirical likelihood for parameter estimation and variable selection in partially linear models with measurement errors in possibly all the variables. By using adaptive Lasso penalty function, we show that penalized empirical likelihood has the oracle property. That is, with probability tending to one, penalized empirical likelihood identifies the true model and estimates
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On variance estimation under shifts in the mean AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-04-01 Ieva Axt, Roland Fried
In many situations, it is crucial to estimate the variance properly. Ordinary variance estimators perform poorly in the presence of shifts in the mean. We investigate an approach based on non-overlapping blocks, which yields good results in change-point scenarios. We show the strong consistency and the asymptotic normality of such blocks-estimators of the variance under independence. Weak consistency
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Relative variation indexes for multivariate continuous distributions on $$[0,\infty )^k$$[0,∞)k and extensions AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-03-09 Célestin C. Kokonendji, Aboubacar Y. Touré, Amadou Sawadogo
We introduce some new indexes to measure the departure of any multivariate continuous distribution on the nonnegative orthant of the corresponding space from a given reference distribution. The reference distribution may be an uncorrelated exponential model. The proposed multivariate variation indexes that are a continuous analogue to the relative Fisher dispersion indexes of multivariate count models
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Nonparametric Archimedean generator estimation with implications for multiple testing AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-03-05 André Neumann, Thorsten Dickhaus
In multiple testing, the family-wise error rate can be bounded under some conditions by the copula of the test statistics. Assuming that this copula is Archimedean, we consider two nonparametric Archimedean generator estimators. More specifically, we use the nonparametric estimator from Genest et al. (Test 20(2):223–256, 2011) and a slight modification thereof. In simulations, we compare the resulting
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A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2020-03-04 Jayabrata Biswas, Pulak Ghosh, Kiranmoy Das
Quantile regression models are typically used for modeling non-Gaussian outcomes, and such models allow quantile-specific inference. While there exists a vast literature on conditional quantile regression (where the model parameters are estimated precisely for one prefixed quantile level), relatively less work has been reported on joint quantile regression. The challenge in joint quantile regression
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Self-excited hysteretic negative binomial autoregression AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-12-24 Mengya Liu, Qi Li, Fukang Zhu
This paper studies an observation-driven model for time series of counts, in which the observations are supposed to follow a negative binomial distribution conditioned on past information with the form of the hysteretic autoregression. As an extension of the classical two-regime threshold process, the hysteretic autoregression enjoys a more flexible regime-switching mechanism. Stability properties
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Whittle-type estimation under long memory and nonstationarity AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-10-30 Ying Lun Cheung, Uwe Hassler
We consider six variants of (local) Whittle estimators of the fractional order of integration d. They follow a limiting normal distribution under stationarity as well as under (a certain degree of) nonstationarity. Experimentally, we observe a lack of continuity of the objective functions of the two fully extended versions at \(d=1/2\) that has not been reported before. It results in a pileup of the
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Model selection in linear mixed-effect models AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-10-28 Simona Buscemi, Antonella Plaia
Linear mixed-effects models are a class of models widely used for analyzing different types of data: longitudinal, clustered and panel data. Many fields, in which a statistical methodology is required, involve the employment of linear mixed models, such as biology, chemistry, medicine, finance and so forth. One of the most important processes, in a statistical analysis, is given by model selection
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Bayesian sensitivity analysis to unmeasured confounding for misclassified data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-09-18 Qi Zhou, Yoo-Mi Chin, James D. Stamey, Joon Jin Song
Bayesian sensitivity analysis of unmeasured confounding is proposed for observational data with misclassified outcome. The approach simultaneously corrects bias from error in the outcome and examines possible change in the exposure effect estimation assuming the presence of a binary unmeasured confounder. We assess the influence of unmeasured confounding on the exposure effect estimation through two
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Testing the dispersion structure of count time series using Pearson residuals AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-09-04 Boris Aleksandrov, Christian H. Weiß
Pearson residuals are a widely used tool for model diagnostics of count time series. Despite their popularity, little is known about their distribution such that statistical inference is problematic. Squared Pearson residuals are considered for testing the conditional dispersion structure of the given count time series. For two popular types of Markov count processes, an asymptotic approximation for
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Directional bivariate quantiles: a robust approach based on the cumulative distribution function AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-08-31 Nadja Klein, Thomas Kneib
The definition of multivariate quantiles has gained considerable attention in previous years as a tool for understanding the structure of a multivariate data cloud. Due to the lack of a natural ordering for multivariate data, many approaches have either considered geometric generalisations of univariate quantiles or data depths that measure centrality of data points. Both approaches provide a centre-outward
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Mallows’ models for imperfect ranking in ranked set sampling AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-07-15 Nikolay I. Nikolov, Eugenia Stoimenova
In this paper, we consider some statistical measures of deviation from the perfect ranking in the framework of ranked set sampling. We use nonparametric approach for testing the null hypothesis for perfect ranking. The distance-based Mallows’ models with appropriate distance on permutations are suggested in the case of imperfect ranking. Some asymptotic results for the corresponding error probability
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Vine copula regression for observational studies AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-06-05 Roger M. Cooke, Harry Joe, Bo Chang
If explanatory variables and a response variable of interest are simultaneously observed, then fitting a joint multivariate density to all variables would enable prediction via conditional distributions. Regular vines or vine copulas with arbitrary univariate margins provide a rich and flexible class of multivariate densities for Gaussian or non-Gaussian dependence structures. The density enables calculation
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KOALA: a new paradigm for election coverage AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-06-04 Alexander Bauer, Andreas Bender, André Klima, Helmut Küchenhoff
Common election poll reporting is often misleading as sample uncertainty is addressed insufficiently or not covered at all. Furthermore, main interest usually lies beyond the simple party shares. For a more comprehensive opinion poll and election coverage, we propose shifting the focus toward the reporting of survey-based probabilities for specific events of interest. We present such an approach for
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A simple approach to construct confidence bands for a regression function with incomplete data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-02-07 Ali Al-Sharadqah, Majid Mojirsheibani
A long-standing problem in the construction of asymptotically correct confidence bands for a regression function \(m(x)=E[Y|X=x]\), where Y is the response variable influenced by the covariate X, involves the situation where Y values may be missing at random, and where the selection probability, the density function f(x) of X, and the conditional variance of Y given X are all completely unknown. This
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A risk perspective of estimating portfolio weights of the global minimum-variance portfolio AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-02-04 Thomas Holgersson, Peter Karlsson, Andreas Stephan
The problem of how to determine portfolio weights so that the variance of portfolio returns is minimized has been given considerable attention in the literature, and several methods have been proposed. Some properties of these estimators, however, remain unknown, and many of their relative strengths and weaknesses are therefore difficult to assess for users. This paper contributes to the field by comparing
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Geometric Tweedie regression models for continuous and semicontinuous data with variation phenomenon AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-01-30 Rahma Abid, Célestin C. Kokonendji, Afif Masmoudi
We introduce a new class of regression models based on the geometric Tweedie models (GTMs) for analyzing both continuous and semicontinuous data, similar to the recent and standard Tweedie regression models. We also present a phenomenon of variation with respect to the equi-varied exponential distribution, where variance is equal to the squared mean. The corresponding power v-functions which characterize
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Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-01-11 Daniel Grabowski, Anna Staszewska-Bystrova, Peter Winker
Inference on impulse response functions from vector autoregressive models is commonly done using bootstrap methods. These methods can be inaccurate in small samples and for persistent processes. This article investigates the construction of skewness-adjusted confidence intervals and joint confidence bands for impulse responses with improved small sample performance. We suggest to adjust the skewness
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A transition model for analyzing multivariate longitudinal data using Gaussian copula approach AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-01-05 Taban Baghfalaki, Mojtaba Ganjali
Longitudinal studies often involve multiple mixed response variables measured repeatedly over time. Although separate modeling of these multiple mixed response variables can be easily performed, they may lead to inefficient estimates and consequently, misleading inferences. For obtaining correct inference, one needs to model multiple mixed responses jointly. In this paper, we use copula models for
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A note on repeated measures analysis for functional data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2019-01-04 Łukasz Smaga
In this paper, the repeated measures analysis for functional data is considered. The known testing procedures for this problem are based on test statistic being the integral of the difference between sample mean functions, which takes into account only “between group variability”. We modify this test statistic to use also information about “within group variability”. More precisely, we construct the
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Correction to: Comparison of joint control schemes for multivariate normal i.i.d. output AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2018-12-18 Manuel Cabral Morais,Wolfgang Schmid,Patrícia Ferreira Ramos,Taras Lazariv,António Pacheco,Ivan Semeniuk
In the original paper, we incorrectly stated that...
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Estimation and variable selection for partial functional linear regression AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2018-12-14 Qingguo Tang, Peng Jin
We propose a new estimation procedure for estimating the unknown parameters and function in partial functional linear regression. The asymptotic distribution of the estimator of the vector of slope parameters is derived, and the global convergence rate of the estimator of unknown slope function is established under suitable norm. The convergence rate of the mean squared prediction error for the proposed
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A unified approach to testing mean vectors with large dimensions AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2018-12-10 M. Rauf Ahmad
A unified testing framework is presented for large-dimensional mean vectors of one or several populations which may be non-normal with unequal covariance matrices. Beginning with one-sample case, the construction of tests, underlying assumptions and asymptotic theory, is systematically extended to multi-sample case. Tests are defined in terms of U-statistics-based consistent estimators, and their limits
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A new approach to truncated regression for count data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2018-12-10 Ana María Martínez-Rodríguez, Antonio Conde-Sánchez, María José Olmo-Jiménez
Standard Poisson and negative binomial truncated regression models for count data include the regressors in the mean of the non-truncated distribution. In this paper, a new approach is proposed so that the explanatory variables determine directly the truncated mean. The main advantage is that the regression coefficients in the new models have a straightforward interpretation as the effect of a change
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A comparison of testing methods in scalar-on-function regression AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2018-10-17 Merve Yasemin Tekbudak, Marcela Alfaro-Córdoba, Arnab Maity, Ana-Maria Staicu
A scalar-response functional model describes the association between a scalar response and a set of functional covariates. An important problem in the functional data literature is to test nullity or linearity of the effect of the functional covariate in the context of scalar-on-function regression. This article provides an overview of the existing methods for testing both the null hypotheses that
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MDCgo takes up the association/correlation challenge for grouped ordinal data AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2018-09-20 Emanuela Raffinetti, Fabio Aimar
The subjective assessment of quality of life, personal skills and the agreement with a certain opinion are common issues in clinical, social, behavioral and marketing research. A wide set of surveys providing ordinal data arises. Beside such variables, other common surveys generate responses on a continuous scale, where the variable actual point value cannot be observed since data belong to certain
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Neyman-type sample allocation for domains-efficient estimation in multistage sampling AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2018-09-19 M. G. M. Khan, Jacek Wesołowski
We consider a problem of allocation of a sample in two- and three-stage sampling. We seek allocation which is both multi-domain and population efficient. Choudhry et al. (Survey Methods 38(1):23–29, 2012) recently considered such problem for one-stage stratified simple random sampling without replacement in domains. Their approach was through minimization of the sample size under constraints on relative
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A joint quantile regression model for multiple longitudinal outcomes AStA. Adv. Stat. Anal. (IF 0.98) Pub Date : 2018-08-10 Hemant Kulkarni, Jayabrata Biswas, Kiranmoy Das
Complexity of longitudinal data lies in the inherent dependence among measurements from same subject over different time points. For multiple longitudinal responses, the problem is challenging due to inter-trait and intra-trait dependence. While linear mixed models are popularly used for analysing such data, appropriate inference on the shape of the population cannot be drawn for non-normal data sets
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