Article
Economics
Juan Gabriel Brida, Bibiana Lanzilotta, Leonardo Moreno
Summary: This study examines the relative expenditure patterns of tourists based on budget allocation and the influence of covariates. It introduces a model to characterize and compare different types of tourists in terms of their budget distribution. The empirical analysis of data from the Inbound Tourism survey in Uruguay reveals that the expenditure patterns in accommodation, food, and other items depend on factors such as destination, season, nationality, and type of accommodation. The inferential analysis also provides insights into different typologies of tourists from a microeconomic perspective.
Article
Computer Science, Artificial Intelligence
Jie Gu, Bin Cui, Shan Lu
Summary: This paper proposes an effective framework for multivariate compositional data classification, utilizing Dirichlet feature embedding to remove data constraint, obtain high-quality training data, and reduce dimensionality, followed by employing support vector machine to build the classification model. Results from simulation study and real-world dataset demonstrate the proposed method achieves good performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Prabuchandran K. J., Nitin Singh, Pankaj Dayama, Ashutosh Agarwal, Vinayaka Pandit
Summary: Change point detection algorithms have a wide range of applications and the paper proposes a parametric approach for change point detection in compositional data. The method performs significantly better on compositional data and is competitive on general data compared to state-of-the-art implementations.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Theory & Methods
Kimmo Suotsalo, Yingying Xu, Jukka Corander, Johan Pensar
Summary: In this paper, an approximate Bayesian approach combining fractional marginal likelihood and pseudo-likelihood is adopted to learn vector autoregressive models. A novel method, PLVAR, is proposed, which is faster and produces more accurate estimates than state-of-the-art methods based on penalized regression. The consistency of the PLVAR estimator is proven, and the method demonstrates attractive performance on both simulated and real-world data.
STATISTICS AND COMPUTING
(2021)
Article
Statistics & Probability
Joaquin Martinez-Minaya, Finn Lindgren, Antonio Lopez-Quilez, Daniel Simpson, David Conesa
Summary: This article introduces a Laplace approximation method for Bayesian inference in Dirichlet regression models, which allows analyzing skewed and heteroscedastic variables on a simplex without data transformation. The article provides theoretical foundations, implementation details, and introduces the dirinla package in R-language for Dirichlet regression. Simulation studies validate the proposed method, and a real data case-study demonstrates its application.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2023)
Article
Statistics & Probability
Mirko Armillotta, Alessandra Luati, Monia Lupparelli
Summary: This paper develops inference for a general class of first order observation-driven models for discrete-valued processes. Stochastic properties are derived under easy-to-check conditions, and consistency and asymptotic normality of quasi-maximum likelihood estimators are established.
ELECTRONIC JOURNAL OF STATISTICS
(2022)
Article
Biology
Alexander Aue, Prabir Burman
Summary: This article presents a method for accurately estimating prediction errors in time series, including both univariate and multivariate stationary time series. By starting from an approximation to the bias-variance decomposition of the prediction error, several estimates are derived for various popular time series models. Simulation experiments show that the proposed estimators perform well in finite samples and can be used for model selection and prediction.
Article
Anthropology
Sa Ren, Xue Wang, Peng Liu, Jian Zhang
Summary: In this manuscript, a method called Dirichlet Process Mixture of Exponential Random Graph Models (DPM-ERGMs) is proposed to model ensembles of networks. The method divides the ensemble into different clusters and models each cluster using a separate Exponential Random Graph Model (ERGM). The number of clusters can be determined automatically and adaptively with the data provided. A Metropolis-within-slice sampling algorithm is developed for full Bayesian inference of DPM-ERGMs.
Article
Computer Science, Theory & Methods
Luca Merlo, Antonello Maruotti, Lea Petrella, Antonio Punzo
Summary: This paper develops a new method for jointly estimating multiple quantiles of multivariate time series. The method considers the correlation structure among outcomes and models the unobserved serial heterogeneity using a hidden semi-Markov chain. Inference is carried out using an efficient algorithm that avoids parametric assumptions about the states' sojourn distributions.
STATISTICS AND COMPUTING
(2022)
Article
Statistics & Probability
Ngai Hang Chan, Wai Leong Ng, Chun Yip Yau
Summary: A self-normalization sequential change-point detection method is proposed for time series analysis, aiming to address issues with traditional tests such as sensitivity to bandwidth parameters and size distortion. The null asymptotic and consistency of the proposed method are established under general regularity conditions, and its effectiveness is demonstrated through simulation experiments and application to railway-bearing temperature data.
Article
Statistics & Probability
Ufuk Beyaztas, Han Lin Shang
Summary: This paper proposes a robust bootstrap algorithm for constructing prediction intervals and forecast regions in autoregressive time series models. The algorithm is based on weighted likelihood estimates and weighted residuals, and its finite sample properties are examined through Monte Carlo studies and empirical data examples.
JOURNAL OF APPLIED STATISTICS
(2022)
Article
Statistics & Probability
Yakoub Boularouk, Jean-Marc Bardet
Summary: This study proposes a Generalized Gaussian Quasi-Maximum Likelihood Estimator for estimating the parameter shape of the generalized gaussian noise in the class of causal time series. Monte Carlo experiments confirm the accuracy of the proposed estimator.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2022)
Article
Health Care Sciences & Services
Annamaria Guolo, Duc-Khanh To
Summary: A new method using pseudo-likelihood for inference on common diagnostic measures is proposed in this study, which assumes working independence between sensitivities and specificities at different thresholds in the same study, overcoming some of the issues in multivariate meta-analysis. Simulation studies demonstrate the satisfactory performance of the method under different scenarios, significantly improving results compared to the multivariate normal counterpart.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2021)
Article
Economics
Zifeng Zhao, Peng Shi, Zhengjun Zhang
Summary: This article introduces a novel multivariate time series model named CuDvine, which models both temporal and cross-sectional dependence simultaneously. By linking multiple uDvines, CuDvine provides a flexible model that can handle complex time series dependency issues.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Economics
Victor Leiva, Helton Saulo, Rubens Souza, Robert G. Aykroyd, Roberto Vila
Summary: The paper introduces a new class of time series models, called BS autoregressive moving average (BISARMA) models, based on the Birnbaum-Saunders (BS) distribution, which allows modeling of positive and asymmetric data with an autoregressive structure. Through a thorough study of the theoretical properties and practical issues of the proposed methodology, performance evaluation using Monte Carlo simulations, and analysis of real-world data, the potential of the method is demonstrated. The numerical results show the excellent performance of the BISARMA model, indicating that the BS distribution is a good modeling choice for time series data with positive support and asymmetric distribution.
JOURNAL OF FORECASTING
(2021)
Article
Statistics & Probability
Tsung- Lin, Wan-Lun Wang
Summary: This paper derives explicit expressions for the moments of truncated multivariate normal/independent distributions with supports confined within a hyper-rectangle. A Monte Carlo experiment is conducted to validate the proposed formulae for five selected members of the distributions.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
Article
Statistics & Probability
Tao Qiu, Qintong Zhang, Yuanyuan Fang, Wangli Xu
Summary: This article introduces a method for testing the homogeneity of two random vectors. The method involves selecting two subspaces and projecting them onto one-dimensional spaces, using the Cramer-von Mises distance to construct the test statistic. The performance is enhanced by repeating this procedure and the effectiveness is demonstrated through numerical simulations.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
Article
Statistics & Probability
Alfredo Alegria, Xavier Emery
Summary: This study contributes to covariance modeling by proposing new parametric families of isotropic matrix-valued functions that exhibit non-monotonic behaviors, such as hole effects and cross-dimples. The benefit of these models is demonstrated on a bivariate dataset of airborne particulate matter concentrations.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
Article
Statistics & Probability
Kento Egashira, Kazuyoshi Yata, Makoto Aoshima
Summary: This study investigates the asymptotic properties of hierarchical clustering in different settings, including high-dimensional, low-sample-size scenarios. The results show that hierarchical clustering exhibits good asymptotic properties under practical settings for high-dimensional data. The study also extends the analysis to consider scenarios where both the dimension and sample size approach infinity, and generalizes the concept of populations in multiclass HDLSS settings.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
Article
Statistics & Probability
Marlene Baumeister, Marc Ditzhaus, Markus Pauly
Summary: This paper introduces a more robust multivariate analysis method by using general quantiles, particularly the median, instead of the traditional mean, and applies and validates this method on various factorial designs. The effectiveness of this method is demonstrated through theoretical and simulation studies on small and moderate sample sizes.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
Article
Statistics & Probability
Chuancun Yin, Narayanaswamy Balakrishnan
Summary: The family of multivariate skew-normal distributions has interesting properties, which also hold for a general class of skew-elliptical distributions.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)
Article
Statistics & Probability
Gaspard Bernard, Thomas Verdebout
Summary: In this paper, we address the problem of testing the relationship between the eigenvalues of a scatter matrix in an elliptical distribution. Using the Le Cam asymptotic theory, we show that the non-specification of nuisance parameters has an asymptotic cost for testing the relationship. We also propose a distribution-free signed-rank test for this problem.
JOURNAL OF MULTIVARIATE ANALYSIS
(2024)