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Statistics and Computing, Volume 29
Volume 29, Number 1, January 2019
- Eduardo García-Portugués
, Michael Sørensen
Langevin diffusions on the torus: estimation and applications. 1-22 - Jingnan Xue, Faming Liang
Double-Parallel Monte Carlo for Bayesian analysis of big data. 23-32 - Lin Su, Howard D. Bondell
Best linear estimation via minimization of relative mean squared error. 33-42 - Cinzia Viroli
Deep Gaussian mixture models. 43-51 - Zhijian He, Lingjiong Zhu:
Asymptotic normality of extensible grid sampling. 53-65 - Matthew C. Edwards, Renate Meyer
Bayesian nonparametric spectral density estimation using B-spline priors. 67-78 - Joshua J. Bon
Fitting monotone polynomials in mixed effects models. 79-98 - Michael Schober
A probabilistic model for the numerical solution of initial value problems. 99-122 - Elmar Spiegel
Generalized additive models with flexible response functions. 123-138 - Eustasio del Barrio
Robust clustering tools based on optimal transportation. 139-160 - Jiaying Gu, Fei Fu, Qing Zhou
Penalized estimation of directed acyclic graphs from discrete data. 161-176 - Yi-An Ma
Irreversible samplers from jump and continuous Markov processes. 177-202
Volume 29, Number 2, March 2019
- Daiane Aparecida Zuanetti
Bayesian nonparametric clustering for large data sets. 203-215 - Moritz Berger
Tree-structured modelling of varying coefficients. 217-229 - Gersende Fort
Stochastic proximal-gradient algorithms for penalized mixed models. 231-253 - Victor Picheny, Rémi Servien
Interpretable sparse SIR for functional data. 255-267 - Dominik Müller
Selection of sparse vine copulas in high dimensions with the Lasso. 269-287 - Noboru Nomura
Orthant probabilities of elliptical distributions from orthogonal projections to subspaces. 289-300 - Alexander Terenin, Shawfeng Dong, David Draper:
GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model. 301-310 - Ruifei Cui
Learning causal structure from mixed data with missing values using Gaussian copula models. 311-333 - Philip L. H. Yu
Rank aggregation using latent-scale distance-based models. 335-349 - Ioulia Papageorgiou
Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables. 351-365 - Xin Luo, Håkon Tjelmeland
Prior specification for binary Markov mesh models. 367-389 - David P. Hofmeyr, Nicos G. Pavlidis
Minimum spectral connectivity projection pursuit - Divisive clustering using optimal projections for spectral clustering. 391-414
Volume 29, Number 3, May 2019
- Mohsen Maleki, Darren Wraith
Robust finite mixture modeling of multivariate unrestricted skew-normal generalized hyperbolic distributions. 415-428 - Andrew G. Salinger, Paul G. Constantine
Gauss-Christoffel quadrature for inverse regression: applications to computer experiments. 429-447 - Florian Maire, Nial Friel
Informed sub-sampling MCMC: approximate Bayesian inference for large datasets. 449-482 - María Xosé Rodríguez-Álvarez
On the estimation of variance parameters in non-standard generalised linear mixed models: application to penalised smoothing. 483-500 - Sabrina Guastavino
A consistent and numerically efficient variable selection method for sparse Poisson regression with applications to learning and signal recovery. 501-516 - Marina I. Knight
Long memory estimation for complex-valued time series. 517-536 - Shonosuke Sugasawa, Genya Kobayashi, Yuki Kawakubo:
Latent mixture modeling for clustered data. 537-548 - Michael Grabchak:
Rejection sampling for tempered Lévy processes. 549-558 - Antonino Abbruzzo
Selecting the tuning parameter in penalized Gaussian graphical models. 559-569 - Junshan Shen, Hanjun Yu, Jin Yang, Catherine Chunling Liu
Semiparametric Bayesian analysis for longitudinal mixed effects models with non-normal AR(1) errors. 571-583 - Jian Cao
Hierarchical-block conditioning approximations for high-dimensional multivariate normal probabilities. 585-598 - Jack Baker
Control variates for stochastic gradient MCMC. 599-615
Volume 29, Number 4, July 2019
- Andrew J. Black
Importance sampling for partially observed temporal epidemic models. 617-630 - Daniel W. Heck
Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. 631-643 - Minwoo Chae
On an algorithm for solving Fredholm integrals of the first kind. 645-654 - Andrés M. Alonso
Clustering time series by linear dependency. 655-676 - Marco Corneli
The dynamic stochastic topic block model for dynamic networks with textual edges. 677-695 - Colin S. Gillespie
Efficient construction of Bayes optimal designs for stochastic process models. 697-706 - Michiel Debruyne, Sebastiaan Höppner, Sven Serneels
Outlyingness: Which variables contribute most? 707-723 - Yuguang Yue
T-optimal designs for multi-factor polynomial regression models via a semidefinite relaxation method. 725-738 - Michael B. Giles
Decision-making under uncertainty: using MLMC for efficient estimation of EVPPI. 739-751 - Marcelo Hartmann
Laplace approximation and natural gradient for Gaussian process regression with heteroscedastic student-t model. 753-773 - Ajay Jasra
Multilevel particle filters for Lévy-driven stochastic differential equations. 775-789 - Michael Fop
Model-based clustering with sparse covariance matrices. 791-819 - Sigrunn Holbek Sørbye
An approximate fractional Gaussian noise model with $$\mathcal {O}(n)$$ O ( n ) computational cost. 821-833 - Xi Chen
Improving the efficiency and robustness of nested sampling using posterior repartitioning. 835-850 - Ajay Jasra, Kody J. H. Law, Prince Peprah Osei:
Correction to: Multilevel particle filters for Lévy-driven stochastic differential equations. 851
Volume 29, Number 5, September 2019
- Benn Macdonald
Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching. 853-867 - Javier Espinosa
A constrained regression model for an ordinal response with ordinal predictors. 869-890 - Edward Higson
Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation. 891-913 - Aleksandar A. Kolev
Inference for ETAS models with non-Poissonian mainshock arrival times. 915-931 - David Gunawan
Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins. 933-946 - Sinan Yildirim
Exact MCMC with differentially private moves - Revisiting the penalty algorithm in a data privacy framework. 947-963 - Øyvind Langsrud
Information preserving regression-based tools for statistical disclosure control. 965-976 - Ruifei Cui
A novel Bayesian approach for latent variable modeling from mixed data with missing values. 977-993 - M. Mehdi Moradi
Resample-smoothing of Voronoi intensity estimators. 995-1010 - Elizabeth Ann Maharaj
Clustering of interval time series. 1011-1034 - Hiroshi Yamashita
Convergence analysis of herded-Gibbs-type sampling algorithms: effects of weight sharing. 1035-1053 - Augustin Touron
Consistency of the maximum likelihood estimator in seasonal hidden Markov models. 1055-1075 - Matthew Heiner
Structured priors for sparse probability vectors with application to model selection in Markov chains. 1077-1093 - Marco Scutari
Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation. 1095-1108 - Joshua Plasse
Multiple changepoint detection in categorical data streams. 1109-1125 - Maria Lomeli
Antithetic and Monte Carlo kernel estimators for partial rankings. 1127-1147 - Andrew Golightly
Efficient sampling of conditioned Markov jump processes. 1149-1163 - Miaoqi Li, Emily Lei Kang
Randomized algorithms of maximum likelihood estimation with spatial autoregressive models for large-scale networks. 1165-1179
Volume 29, Number 6, November 2019
- Mark A. Girolami
Editorial: special edition on probabilistic numerics. 1181-1183 - Gene Ryan Yoo, Houman Owhadi:
De-noising by thresholding operator adapted wavelets. 1185-1201 - Martin Ehler
Optimal Monte Carlo integration on closed manifolds. 1203-1214 - R. Jagadeeswaran, Fred J. Hickernell
Fast automatic Bayesian cubature using lattice sampling. 1215-1229 - Toni Karvonen
Symmetry exploits for Bayesian cubature methods. 1231-1248 - Simon Bartels, Jon Cockayne
Probabilistic linear solvers: a unifying view. 1249-1263 - Han Cheng Lie
Strong convergence rates of probabilistic integrators for ordinary differential equations. 1265-1283 - Oksana A. Chkrebtii
Adaptive step-size selection for state-space probabilistic differential equation solvers. 1285-1295 - Filip Tronarp
Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective. 1297-1315 - Toni Karvonen
On the positivity and magnitudes of Bayesian quadrature weights. 1317-1333 - Chris J. Oates, Timothy John Sullivan:
A modern retrospective on probabilistic numerics. 1335-1351
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