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Stat. Methods Appl.
Publications
volume 28, number 1, 2019
Thomas Suesse
,
Ivy Liu
Mantel-Haenszel estimators of a common odds ratio for multiple response data.
Stat. Methods Appl.
28 (1) (2019)
Patrick Marsh
Nonparametric series density estimation and testing.
Stat. Methods Appl.
28 (1) (2019)
Fabio Bellini
,
Ilia Negri
,
Mariya Pyatkova
Backtesting VaR and expectiles with realized scores.
Stat. Methods Appl.
28 (1) (2019)
Adelchi Azzalini
,
Hyoung-Moon Kim
,
Hea-Jung Kim
Sample selection models for discrete and other non-Gaussian response variables.
Stat. Methods Appl.
28 (1) (2019)
Roberto Benedetti
,
Maria Simona Andreano
,
Federica Piersimoni
Sample selection when a multivariate set of size measures is available.
Stat. Methods Appl.
28 (1) (2019)
Neska El Haouij
,
Jean-Michel Poggi
,
Raja Ghozi
,
Sylvie Sevestre-Ghalila
,
Mériem Jaïdane
Random forest-based approach for physiological functional variable selection for driver's stress level classification.
Stat. Methods Appl.
28 (1) (2019)
Patrick Marsh
Correction to: Nonparametric series density estimation and testing.
Stat. Methods Appl.
28 (1) (2019)
Lu Deng
,
Wendy Lou
,
Nicholas Mitsakakis
Modeling right-censored medical cost data in regression and the effects of covariates.
Stat. Methods Appl.
28 (1) (2019)
Michela Battauz
On Wald tests for differential item functioning detection.
Stat. Methods Appl.
28 (1) (2019)
volume 28, number 3, 2019
Ron S. Kenett
A review of: The class of CUB models: statistical foundations, inferential issues and empirical evidence by Domenico Piccolo and Rosaria Simone.
Stat. Methods Appl.
28 (3) (2019)
Tommaso Proietti
Discussion of The class of CUB models: statistical foundations, inferential issues and empirical evidence - by D. Piccolo and R. Simone.
Stat. Methods Appl.
28 (3) (2019)
Haruhiko Ogasawara
The multiple Cantelli inequalities.
Stat. Methods Appl.
28 (3) (2019)
Alan Agresti
,
Maria Kateri
The class of CUB models: statistical foundations, inferential issues and empirical evidence.
Stat. Methods Appl.
28 (3) (2019)
Domenico Piccolo
,
Rosaria Simone
The class of cub models: statistical foundations, inferential issues and empirical evidence.
Stat. Methods Appl.
28 (3) (2019)
Marica Manisera
,
Paola Zuccolotto
Discussion of "The class of cub models: statistical foundations, inferential issues and empirical evidence" by Domenico Piccolo and Rosaria Simone.
Stat. Methods Appl.
28 (3) (2019)
Rainer Hirk
,
Kurt Hornik
,
Laura Vana
Multivariate ordinal regression models: an analysis of corporate credit ratings.
Stat. Methods Appl.
28 (3) (2019)
Roberto Colombi
,
Sabrina Giordano
,
Anna Gottard
Discussion of "The class of CUB models: statistical foundations, inferential issues and empirical evidence".
Stat. Methods Appl.
28 (3) (2019)
Gerhard Tutz
Comments on The class of cub models: statistical foundations, inferential issues and empirical evidence by D. Piccolo and R. Simone.
Stat. Methods Appl.
28 (3) (2019)
Leonardo Grilli
,
Carla Rampichini
Discussion of 'The class of CUB models: statistical foundations, inferential issues and empirical evidence' by Domenico Piccolo and Rosaria Simone.
Stat. Methods Appl.
28 (3) (2019)
Domenico Piccolo
,
Rosaria Simone
Rejoinder to the discussion of "The class of cub models: statistical foundations, inferential issues and empirical evidence".
Stat. Methods Appl.
28 (3) (2019)
Guido Bulligan
,
Lorenzo Burlon
,
Davide Delle Monache
,
Andrea Silvestrini
Real and financial cycles: estimates using unobserved component models for the Italian economy.
Stat. Methods Appl.
28 (3) (2019)
Francesco Bartolucci
,
Fulvia Pennoni
Comment on: The class of CUB models: statistical foundations, inferential issues and empirical evidence.
Stat. Methods Appl.
28 (3) (2019)
Tommaso Proietti
Editorial.
Stat. Methods Appl.
28 (3) (2019)
volume 27, number 2, 2018
Richard William Farebrother
Correction to: A genealogy of Florence Nightingale, Charles Darwin, Francis Galton and Francis Ysidro Edgeworth with special reference to their Italian connections and an annexe on Beatrice Webb and Charles Booth.
Stat. Methods Appl.
27 (2) (2018)
Catia Scricciolo
-Wasserstein deconvolution of Laplace mixtures.
Stat. Methods Appl.
27 (2) (2018)
Raffaele Argiento
,
Matteo Ruggiero
Computational challenges and temporal dependence in Bayesian nonparametric models.
Stat. Methods Appl.
27 (2) (2018)
volume 27, number 3, 2018
Sergio Longobardi
,
Patrizia Falzetti
,
Margherita Maria Pagliuca
Quis custiodet ipsos custodes? How to detect and correct teacher cheating in Italian student data.
Stat. Methods Appl.
27 (3) (2018)
Chen Yang
,
Wenjun Jiang
,
Jiang Wu
,
Xin Liu
,
Zhichuan Li
Clustering of financial instruments using jump tail dependence coefficient.
Stat. Methods Appl.
27 (3) (2018)
Jean-Paul Chavas
On multivariate quantile regression analysis.
Stat. Methods Appl.
27 (3) (2018)
Lukasz Smaga
,
Hidetoshi Matsui
A note on variable selection in functional regression via random subspace method.
Stat. Methods Appl.
27 (3) (2018)
Giulia Barbati
,
Alessio Farcomeni
Prognostic assessment of repeatedly measured time-dependent biomarkers, with application to dilated cardiomyopathy.
Stat. Methods Appl.
27 (3) (2018)
Raju Maiti
,
Atanu Biswas
,
Bibhas Chakraborty
Modelling of low count heavy tailed time series data consisting large number of zeros and ones.
Stat. Methods Appl.
27 (3) (2018)
Ruidong Han
,
Xinghui Wang
,
Shuhe Hu
Asymptotics of the weighted least squares estimation for AR(1) processes with applications to confidence intervals.
Stat. Methods Appl.
27 (3) (2018)
Haejune Oh
,
Sangyeol Lee
On score vector- and residual-based CUSUM tests in ARMA-GARCH models.
Stat. Methods Appl.
27 (3) (2018)
Ali Karimnezhad
,
Ahmad Parsian
Most stable sample size determination in clinical trials.
Stat. Methods Appl.
27 (3) (2018)
volume 27, number 4, 2018
Christophe Croux
Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample" by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini.
Stat. Methods Appl.
27 (4) (2018)
Stephane Heritier
,
Maria-Pia Victoria-Feser
Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample" by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini.
Stat. Methods Appl.
27 (4) (2018)
Junke Kou
,
Youming Liu
Wavelet regression estimations with strong mixing data.
Stat. Methods Appl.
27 (4) (2018)
Ricardo A. Maronna
,
Victor J. Yohai
Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample" by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini.
Stat. Methods Appl.
27 (4) (2018)
Claudio Agostinelli
,
Luca Greco
Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample" by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini.
Stat. Methods Appl.
27 (4) (2018)
Simon J. Sheather
,
Joseph W. McKean
Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample".
Stat. Methods Appl.
27 (4) (2018)
Valentin Todorov
Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample" by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini.
Stat. Methods Appl.
27 (4) (2018)
Andrea Cerioli
,
Marco Riani
,
Anthony C. Atkinson
,
Aldo Corbellini
Rejoinder to the discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample".
Stat. Methods Appl.
27 (4) (2018)
Domenico Perrotta
,
Francesca Torti
Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample".
Stat. Methods Appl.
27 (4) (2018)
Andrea Cerioli
,
Marco Riani
,
Anthony C. Atkinson
,
Aldo Corbellini
The power of monitoring: how to make the most of a contaminated multivariate sample.
Stat. Methods Appl.
27 (4) (2018)
Alessio Farcomeni
,
Francesco Dotto
The power of (extended) monitoring in robust clustering - Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample" by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini.
Stat. Methods Appl.
27 (4) (2018)
Hee-Young Kim
,
Christian H. Weiß
,
Tobias A. Möller
Testing for an excessive number of zeros in time series of bounded counts.
Stat. Methods Appl.
27 (4) (2018)
Luis Angel García-Escudero
,
Alfonso Gordaliza
,
Carlos Matrán
,
Agustín Mayo-Íscar
Comments on "The power of monitoring: how to make the most of a contaminated multivariate sample".
Stat. Methods Appl.
27 (4) (2018)
Giovana Fumes-Ghantous
,
Silvia L. P. Ferrari
,
José Eduardo Corrente
Box-Cox t random intercept model for estimating usual nutrient intake distributions.
Stat. Methods Appl.
27 (4) (2018)
Jakob Raymaekers
,
Peter J. Rousseeuw
,
Iwein Vranckx
Discussion of "The power of monitoring: how to make the most of a contaminated multivariate sample".
Stat. Methods Appl.
27 (4) (2018)