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Fast, effective, and coherent time series modelling using the sparsity-ranked lasso Stat. Model. (IF 1.0) Pub Date : 2024-03-08 Ryan Peterson, Joseph Cavanaugh
The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet of the SRL is that an algorithm should be more sceptical of higher-order polynomials and interactions a priori compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In time series, the
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Taking advantage of sampling designs in spatial small-area survey studies Stat. Model. (IF 1.0) Pub Date : 2024-03-05 Carlos Vergara-Hernández, Marc Marí-Dell’Olmo, Laura Oliveras, Miguel Angel Martinez-Beneito
Spatial small area estimation models have become very popular in some contexts, such as disease mapping. Data in disease mapping studies are exhaustive, that is, the available data are supposed to be a complete register of all the observable events. In contrast, some other small area studies do not use exhaustive data, such as survey based studies, where a particular sampling design is typically followed
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Copula-based pairwise estimator for quantile regression with hierarchical missing data Stat. Model. (IF 1.0) Pub Date : 2024-02-28 Anneleen Verhasselt, Alvaro J. Flórez, Geert Molenberghs, Ingrid Van Keilegom
Quantile regression can be a helpful technique for analysing clustered (such as longitudinal) data. It can characterize the change in response over time without making distributional assumptions and is robust to outliers in the response. A quantile regression model using a copula-based multivariate asymmetric Laplace distribution for addressing correlation due to clustering is introduced. Furthermore
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Estimation for vector autoregressive model under multivariate skew-t-normal innovations Stat. Model. (IF 1.0) Pub Date : 2024-02-15 Uchenna Chinedu Nduka, Everestus Okafor Ossai, Mbanefo Solomon Madukaife, Tobias Ejiofor Ugah
Current procedures for estimating the parameters of [Formula: see text]th order vector autoregressive (VAR [Formula: see text]) model are usually based on assuming that the ensuing error distribution is multivariate normal. But there exists large body of evidence that several data encountered in real life are skewed; thereby making estimators derived based on normality assumption not suitable in such
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Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients Stat. Model. (IF 1.0) Pub Date : 2024-02-08 Federica Cugnata, Chiara Brombin, Pietro E. Cippà, Alessandro Ceschi, Paolo Ferrari, Clelia Di Serio
When modelling the dynamics of biomarkers in biomedical studies, it is essential to identify homogeneous clusters of patients and analyse them from a precision medicine perspective. This need has emerged as crucial and urgent during the COVID-19 pandemic: early understanding of symptoms and patient heterogeneity has significant implications for prevention, early diagnosis, effective management, and
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Impact of jittering on raster- and distance-based geostatistical analyses of DHS data Stat. Model. (IF 1.0) Pub Date : 2024-02-07 Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad
Fine-scale covariate rasters are routinely used in geostatistical models for mapping demographic and health indicators based on household surveys from the Demographic and Health Surveys (DHS) program. However, the geostatistical analyses ignore the fact that GPS coordinates in DHS surveys are jittered for privacy purposes. We demonstrate the need to account for this jittering, and we propose a computationally
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A multilevel analysis of real estate valuation using distributional and quantile regression Stat. Model. (IF 1.0) Pub Date : 2023-04-18 Alexander Razen, Wolfgang Brunauer, Nadja Klein, Thomas Kneib, Stefan Lang, Nikolaus Umlauf
Real estate valuation is typically based on hedonic regression models where the expected price of a property is explained in dependence of its attributes. However, investors in the housing market a...
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Multidimensional beta-binomial regression model: A joint analysis of patient-reported outcomes Stat. Model. (IF 1.0) Pub Date : 2023-04-14 Josu Najera-Zuloaga, Dae-Jin Lee, Cristobal Esteban, Inmaculada Arostegui
Patient-reported outcomes (PROs) are often used as primary outcomes in clinical research studies. PROs are usually measured in ordinal scales and they tend to have excess variability beyond the bin...
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Power logit regression for modeling bounded data Stat. Model. (IF 1.0) Pub Date : 2023-02-13 Francisco F. Queiroz, Silvia L. P. Ferrari
The main purpose of this article is to introduce a new class of regression models for bounded continuous data, commonly encountered in applied research. The models, named the power logit regression...
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A two-part measurement error model to estimate participation in undeclared work and related earnings Stat. Model. (IF 1.0) Pub Date : 2023-02-07 Maria Felice Arezzo, Serena Arima, Giuseppina Guagnano
In undeclared work research, the estimation of the magnitude of the phenomenon (i.e., the amount of income and/or the percentage of workers involved) is of major interest. This has been done either...
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Soft-clipping INGARCH models for time series of bounded counts Stat. Model. (IF 1.0) Pub Date : 2022-12-20 Christian H. Weiß, Malte Jahn
The soft-clipping binomial INGARCH (scBINGARCH) models are proposed as time series models for bounded counts, which have a nearly linear structure and also allow for negative autocor-relations. Con...
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Covariate-modulated rectangular latent Markov models with an unknown number of regime profiles Stat. Model. (IF 1.0) Pub Date : 2022-11-16 Alfonso Russo, Alessio Farcomeni, Maria Grazia Pittau, Roberto Zelli
We derive a multivariate latent Markov model with number of latent states that can possibly change at each time point. We model both the manifest and latent distributions conditionally on explanato...
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Statistical modelling of COVID-19 data: Putting generalized additive models to work Stat. Model. (IF 1.0) Pub Date : 2022-09-28 Cornelius Fritz, Giacomo De Nicola, Martje Rave, Maximilian Weigert, Yeganeh Khazaei, Ursula Berger, Helmut Küchenhoff, Göran Kauermann
Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further s...
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Semi-parametric hidden Markov model for large-scale multiple testing under dependency Stat. Model. (IF 1.0) Pub Date : 2022-09-27 Joungyoun Kim, Johan Lim, Jong Soo Lee
In this article, we propose a new semiparametric hidden Markov model (HMM) for use in the simultaneous hypothesis testing with dependency. The semi- or non-parametric HMM in the literature requires...
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Bayesian discrete conditional transformation models Stat. Model. (IF 1.0) Pub Date : 2022-09-22 Manuel Carlan, Thomas Kneib
We propose a novel Bayesian model framework for discrete ordinal and count data based on conditional transformations of the responses. The conditional transformation function is estimated from the ...
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Recurrent events analysis with piece-wise exponential additive mixed models Stat. Model. (IF 1.0) Pub Date : 2022-09-08 Jordache Ramjith, Andreas Bender, Kit C. B. Roes, Marianne A. Jonker
Recurrent events analysis plays an important role in many applications, including the study of chronic diseases or recurrence of infections. Historically, many models for recurrent events have been...
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Multi-parameter regression survival modelling with random effects Stat. Model. (IF 1.0) Pub Date : 2022-09-07 Fatima-Zahra Jaouimaa, Il Do Ha, Kevin Burke
We consider a parametric modelling approach for survival data where covariates are allowed to enter the model through multiple distributional parameters (i.e., scale and shape). This is in contrast...
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Response mixture models based on supervised components: Clustering floristic taxa Stat. Model. (IF 1.0) Pub Date : 2022-09-05 Julien Gibaud, Xavier Bry, Catherine Trottier, Frédéric Mortier, Maxime Réjou-Méchain
In this article, we propose to cluster responses in order to identify groups predicted by specific explanatory components. A response matrix is assumed to depend on a set of explanatory variables a...
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A model for space-time threshold exceedances with an application to extreme rainfall Stat. Model. (IF 1.0) Pub Date : 2022-05-27 Paola Bortot, Carlo Gaetan
In extreme value studies, models for observations exceeding a fixed high threshold have the advantage of exploiting the available extremal information while avoiding bias from low values. In the co...
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Self-exciting point process modelling of crimes on linear networks Stat. Model. (IF 1.0) Pub Date : 2022-05-19 Nicoletta D’Angelo, David Payares, Giada Adelfio, Jorge Mateu
Although there are recent developments for the analysis of first and second-order characteristics of point processes on networks, there are very few attempts in introducing models for network data....
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Interpretable modelling of retail demand and price elasticity for passenger flights using booking data Stat. Model. (IF 1.0) Pub Date : 2022-05-09 Jan Felix Meyer, Go¨ran Kauermann, Michael Stanley Smith
We propose a model of retail demand for air travel and ticket price elasticity at the daily booking and individual flight level. Daily bookings are modelled as a non-homogeneous Poisson process wit...
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On Lasso and adaptive Lasso for non-random sample in credit scoring Stat. Model. (IF 1.0) Pub Date : 2022-05-09 Emmanuel O. Ogundimu
Prediction models in credit scoring are often formulated using available data on accepted applicants at the loan application stage. The use of this data to estimate probability of default (PD) may ...
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A time-varying GARCH mixed-effects model for isolating high- and low- frequency volatility and co-volatility Stat. Model. (IF 1.0) Pub Date : 2022-03-14 Zeynab Aghabazaz, Iraj Kazemi, Alireza Nematollahi
This article studies long-term, short-term volatility and co-volatility in stock markets by introducing modelling strategies to the multivariate data analysis that deal with serially correlated inn...
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Bayesian modelling of integer-valued transfer function models Stat. Model. (IF 1.0) Pub Date : 2022-03-01 Aljo Clair Pingal, Cathy W. S. Chen
External events are commonly known as interventions that often affect times series of counts. This research introduces a class of transfer function models that include four different types of inter...
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Editorial Stat. Model. (IF 1.0) Pub Date : 2022-02-08 Vicente Núñez-Antón, Andreas Mayr, Francesco Bartolucci
First of all, the editors hope that this reaches everyone well and safe during this unusual COVID-19 year. We also invite you to act individually, under these extraordinary circumstances, to renew your subscription for the current year. You can act at the website: http://www.statmod.org/join.htm, where you can get a variety of modelling information. We want to ensure uninterrupted delivery of our journal
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Guest Editorial: Special Issue in Honour of Murray Aitkin Stat. Model. (IF 1.0) Pub Date : 2022-02-08 Brian Francis, John Hinde
We are delighted to introduce this special issue of Statistical Modelling to mark Murray Aitkin’s 80th birthday featuring papers by some of Murray’s friends and colleagues. We have both known Murray for over 40 years and while he may have aged remarkably little over this period his interests in statistical modelling have been wide-ranging and his inferential approaches have developed in a very particular
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Obituary: Brian Douglas Marx Stat. Model. (IF 1.0) Pub Date : 2022-02-08 Paul Eilers, Emmanuel Lesaffre
On 25 November 2021, Brian Douglas Marx passed away at the age of 61, after a short illness. He was survived by his spouse Alexandra, his children Sophia and Leopold, his mother, and his sisters.
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Maximum approximate likelihood estimation of general continuous-time state-space models Stat. Model. (IF 1.0) Pub Date : 2022-01-16 Sina Mews, Roland Langrock, Marius Ötting, Houda Yaqine, Jost Reinecke
Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time
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Dynamic modelling of corporate credit ratings and defaults Stat. Model. (IF 1.0) Pub Date : 2021-12-17 Laura Vana, Kurt Hornik
In this article, we propose a longitudinal multivariate model for binary and ordinal outcomes to describe the dynamic relationship among firm defaults and credit ratings from various raters. The latent probability of default is modelled as a dynamic process which contains additive firm-specific effects, a latent systematic factor representing the business cycle and idiosyncratic observed and unobserved
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Nonlinear discrete-time hazard models for women's entry into marriage Stat. Model. (IF 1.0) Pub Date : 2021-12-14 Heather L. Turner, Andy D. Batchelor, David Firth
We propose a hazard model for entry into marriage, based on a bell-shaped function to model the dependence on age. We demonstrate near-aliasing in an extension that estimates the support of the hazard and mitigate this via re-parameterization. Our proposed model parameterizes the maximum hazard and corresponding age, thereby facilitating more general models where these features depend on covariates
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Multivariate functional additive mixed models Stat. Model. (IF 1.0) Pub Date : 2021-12-07 Alexander Volkmann, Almond Stöcker, Fabian Scheipl, Sonja Greven
Multivariate functional data can be intrinsically multivariate like movement trajectories in 2D or complementary such as precipitation, temperature and wind speeds over time at a given weather station. We propose a multivariate functional additive mixed model (multiFAMM) and show its application to both data situations using examples from sports science (movement trajectories of snooker players) and
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Smoothing spatio-temporal data with complex missing data patterns Stat. Model. (IF 1.0) Pub Date : 2021-12-02 Eleonora Arnone, Laura M. Sangalli, Andrea Vicini
We consider spatio-temporal data and functional data with spatial dependence, characterized by complicated missing data patterns. We propose a new method capable to efficiently handle these data structures, including the case where data are missing over large portions of the spatio-temporal domain. The method is based on regression with partial differential equation regularization. The proposed model
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Reflections on statistical modelling: A conversation with Murray Aitkin Stat. Model. (IF 1.0) Pub Date : 2021-11-25 Murray Aitkin, John Hinde, Brian Francis
A virtual interview with Murray Aitkin by Brian Francis and John Hinde, two of the original members of the Centre for Applied Statistics that Murray created at Lancaster University. The talk ranges over Murray's reflections of a career in statistical modelling and the many different collaborations across the world that have been such a significant part of it.
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Bayesian analysis of two-part nonlinear latent variable model: Semiparametric method Stat. Model. (IF 1.0) Pub Date : 2021-11-24 Jian-Wei Gou, Ye-Mao Xia, De-Peng Jiang
Two-part model (TPM) is a widely appreciated statistical method for analyzing semi-continuous data. Semi-continuous data can be viewed as arising from two distinct stochastic processes: one governs the occurrence or binary part of data and the other determines the intensity or continuous part. In the regression setting with the semi-continuous outcome as functions of covariates, the binary part is
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Bayesian clustered coefficients regression with auxiliary covariates assistant random effects Stat. Model. (IF 1.0) Pub Date : 2021-10-28 Guanyu Hu, Yishu Xue, Zhihua Ma
In regional economics research, a problem of interest is to detect similarities between regions, and estimate their shared coefficients in economics models. In this article, we propose a mixture of finite mixtures clustered regression model with auxiliary covariates that account for similarities in demographic or economic characteristics over a spatial domain. Our Bayesian construction provides both
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Robust clustering based on finite mixture of multivariate fragmental distributions Stat. Model. (IF 1.0) Pub Date : 2021-10-22 Mohsen Maleki, Geoffrey J. McLachlan, Sharon X. Lee
A flexible class of multivariate distributions called scale mixtures of fragmental normal (SMFN) distributions, is introduced. Its extension to the case of a finite mixture of SMFN (FM-SMFN) distributions is also proposed. The SMFN family of distributions is convenient and effective for modelling data with skewness, discrepant observations and population heterogeneity. It also possesses some other
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A joint transition model for evaluating eGFR as biomarker for rejection after kidney transplantation Stat. Model. (IF 1.0) Pub Date : 2021-10-14 Maarten Coemans, Geert Verbeke, Maarten Naesens
The estimated glomerular filtration rate (eGFR) quantifies kidney graft function and is measured repeatedly after transplantation. Kidney graft rejection is diagnosed by performing biopsies on a regular basis (protocol biopsies at time of stable eGFR) or by performing biopsies due to clinical cause (indication biopsies at time of declining eGFR). The diagnostic value of the eGFR evolution as biomarker
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Canonical correlation analysis in high dimensions with structured regularization Stat. Model. (IF 1.0) Pub Date : 2021-10-03 Elena Tuzhilina, Leonardo Tozzi, Trevor Hastie
Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an ℓ2 penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features
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Outlier accommodation with semiparametric density processes: A study of Antarctic snow density modelling Stat. Model. (IF 1.0) Pub Date : 2021-09-29 Daniel M. Sheanshang, Philip A. White, Durban G. Keeler
In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust methods are often too simple for some applications. In our motivating application, scientists drill snow
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A Bayesian framework for modelling the preferential selection process in respondent-driven sampling Stat. Model. (IF 1.0) Pub Date : 2021-09-22 Katherine R. McLaughlin
In sampling designs that utilize peer recruitment, the sampling process is partially unknown and must be modelled to make inference about the population and estimate standard outcomes like prevalence. We develop a Bayesian model for the recruitment process for respondent-driven sampling (RDS), a network sampling methodology used worldwide to sample hidden populations that are not reachable by conventional
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Mixture models and networks: The stochastic blockmodel Stat. Model. (IF 1.0) Pub Date : 2021-09-04 Giacomo De Nicola, Benjamin Sischka, Göran Kauermann
Mixture models are probabilistic models aimed at uncovering and representing latent subgroups within a population. In the realm of network data analysis, the latent subgroups of nodes are typically identified by their connectivity behaviour, with nodes behaving similarly belonging to the same community. In this context, mixture modelling is pursued through stochastic blockmodelling. We consider stochastic
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Modelling agreement for binary intensive longitudinal data Stat. Model. (IF 1.0) Pub Date : 2021-09-03 Sophie Vanbelle, Emmanuel Lesaffre
Devices that measure our physical, medical and mental condition have entered our daily life recently. Such devices measure our status in a continuous manner and can be useful in predicting future medical events or can guide us towards a healthier life. It is therefore important to establish that such devices record our behaviour in a reliable manner and measure what we believe they measure. In this
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Parametric estimation of non-crossing quantile functions Stat. Model. (IF 1.0) Pub Date : 2021-09-01 Gianluca Sottile, Paolo Frumento
Quantile regression (QR) has gained popularity during the last decades, and is now considered a standard method by applied statisticians and practitioners in various fields. In this work, we applied QR to investigate climate change by analysing historical temperatures in the Arctic Circle. This approach proved very flexible and allowed to investigate the tails of the distribution, that correspond to
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Random effect models for multivariate mixed data: A Parafac-based finite mixture approach Stat. Model. (IF 1.0) Pub Date : 2021-09-01 Marco Alfò, Paolo Giordani
We discuss a flexible regression model for multivariate mixed responses. Dependence between outcomes is introduced via the joint distribution of discrete outcome- and individual-specific random effects that represent potential unobserved heterogeneity in each outcome profile. A different number of locations can be used for each margin, and the association structure is described by a tensor that can
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On Bayesian model selection for INGARCH models viatrans-dimensional Markov chain Monte Carlo methods Stat. Model. (IF 1.0) Pub Date : 2021-08-30 Panagiota Tsamtsakiri, Dimitris Karlis
There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends
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Mixed effect modelling and variable selection for quantile regression Stat. Model. (IF 1.0) Pub Date : 2021-08-23 Haim Bar, James G. Booth, Martin T. Wells
It is known that the estimating equations for quantile regression (QR) can be solved using an EM algorithm in which the M-step is computed via weighted least squares, with weights computed at the E-step as the expectation of independent generalized inverse-Gaussian variables. This fact is exploited here to extend QR to allow for random effects in the linear predictor. Convergence of the algorithm in
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Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models Stat. Model. (IF 1.0) Pub Date : 2021-08-18 Lennart Oelschläger, Timo Adam
Financial markets exhibit alternating periods of rising and falling prices. Stock traders seeking to make profitable investment decisions have to account for those trends, where the goal is to accurately predict switches from bullish to bearish markets and vice versa. Popular tools for modelling financial time series are hidden Markov models, where a latent state process is used to explicitly model
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Withdrawal: Administrative Duplicate Publication Stat. Model. (IF 1.0) Pub Date : 2021-07-12
SAGE Publishing regrets that due to an administrative error, this article was accidentally published Online First and in Volume 20 Issue 6 with different DOIs. There was no duplication of the article in the printed and online version of Volume 20 Issue 6.
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Principal component regression in GAMLSS applied to Greek–German government bond yield spreads Stat. Model. (IF 1.0) Pub Date : 2021-06-21 D. Stasinopoulos Mikis, A. Rigby Robert, Georgikopoulos Nikolaos, De Bastiani Fernanda
A solution to the problem of having to deal with a large number of interrelated explanatory variables within a generalized additive model for location, scale and shape (GAMLSS) is given here using as an example the Greek–German government bond yield spreads from 25 April 2005 to 31 March 2010. Those were turbulent financial years, and in order to capture the spreads behaviour, a model has to be able
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A spatially explicit N-mixture model for the estimation of disease prevalence Stat. Model. (IF 1.0) Pub Date : 2021-06-20 Ben J Brintz, Lisa Madsen, Claudio Fuentes
This article develops an approximate N-mixture model for infectious disease counts that accounts for under-reporting as well as spatial dependence induced by person-to-person spread of disease. We employ the model to estimate actual case counts in Oregon of chlamydia, an easily-treated but usually asymptomatic sexually transmitted disease. We describe a combined parametric bootstrap to account for
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Quantile regression for longitudinal data via the multivariate generalized hyperbolic distribution Stat. Model. (IF 1.0) Pub Date : 2021-06-07 Alvaro J. Flórez, Ingrid Van Keilegom, Geert Molenberghs, Anneleen Verhasselt
While extensive research has been devoted to univariate quantile regression, this is considerably less the case for the multivariate (longitudinal) version, even though there are many potential applications, such as the joint examination of growth curves for two or more growth characteristics, such as body weight and length in infants. Quantile functions are easier to interpret for a population of
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Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India Stat. Model. (IF 1.0) Pub Date : 2021-05-31 Aritz Adin, Tomás Goicoa, James S. Hodges, Patrick M. Schnell, María D. Ugarte
Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice
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Interactively visualizing distributional regression models with distreg.vis Stat. Model. (IF 1.0) Pub Date : 2021-05-27 Stanislaus Stadlmann, Thomas Kneib
A newly emerging field in statistics is distributional regression, where not only the mean but each parameter of a parametric response distribution can be modelled using a set of predictors. As an extension of generalized additive models, distributional regression utilizes the known link functions (log, logit, etc.), model terms (fixed, random, spatial, smooth, etc.) and available types of distributions
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Bayesian adjustment for measurement error in an offset variable in a Poisson regression model Stat. Model. (IF 1.0) Pub Date : 2021-05-24 Kangjie Zhang, Juxin Liu, Yang Liu, Peng Zhang, Raymond J. Carroll
Fatal car crashes are the leading cause of death among teenagers in the USA. The Graduated Driver Licensing (GDL) programme is one effective policy for reducing the number of teen fatal car crashes. Our study focuses on the number of fatal car crashes in Michigan during 1990–2004 excluding 1997, when the GDL started. We use Poisson regression with spatially dependent random effects to model the county
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A regularized hidden Markov model for analyzing the ‘hot shoe’ in football Stat. Model. (IF 1.0) Pub Date : 2021-05-19 Marius Ötting, Groll Andreas
We propose a penalized likelihood approach in hidden Markov models (HMMs) to perform automated variable selection. To account for a potential large number of covariates, which also may be substantially correlated, we consider the elastic net penalty containing LASSO and ridge as special cases. By quadratically approximating the non-differentiable penalty, we ensure that the likelihood can be maximized
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Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach Stat. Model. (IF 1.0) Pub Date : 2021-04-07 Luca Merlo, Antonello Maruotti, Lea Petrella
This article develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable to also influence the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the
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Modelling changes over time in a multivariate paired comparison: An application to window display design Stat. Model. (IF 1.0) Pub Date : 2021-03-31 Alexandra Grand, Regina Dittrich
This article proposes an alternative method of making comparative judgements in multivariate paired comparisons (PCs) where judgements about change are made directly by comparing an object at two time points for each of a series of attributes. The application deals with the design of shop window displays where products should be arranged by teams of vocational students according to aesthetic principles
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Semi-supervised clustering of time-dependent categorical sequences with application to discovering education-based life patterns Stat. Model. (IF 1.0) Pub Date : 2021-03-08 Yingying Zhang, Volodymyr Melnykov, Igor Melnykov
A new approach to the analysis of heterogeneous categorical sequences is proposed. The first-order Markov model is employed in a finite mixture setting with initial state and transition probabilities being expressed as functions of time. The expectation–maximization algorithm approach to parameter estimation is implemented in the presence of positive equivalence constraints that determine which observations
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Reflections on Murray Aitkin's contributions to nonparametric mixture models and Bayes factors Stat. Model. (IF 1.0) Pub Date : 2021-02-08 Alan Agresti, Francesco Bartolucci, Antonietta Mira
We describe two interesting and innovative strands of Murray Aitkin's research publications, dealing with mixture models and with Bayesian inference. Of his considerable publications on mixture models, we focus on a nonparametric random effects approach in generalized linear mixed modelling, which has proven useful in a wide variety of applications. As an early proponent of ways of implementing the
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Renewal model for anomalous traffic in Internet2 links Stat. Model. (IF 1.0) Pub Date : 2021-02-01 John Nicholson, Piotr Kokoszka, Robert Lund, Peter Kiessler, Julia Sharp
We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical