yusuke1997/dblp-id-title huggingface dataset:
Evaluation of vectorization/parallelization techniques: application to nonparametric curve estimation
A hypergraph-theoretic analysis of collapsibility and decomposability for extended log-linear models
SB-ETAS: using simulation based inference for scalable, likelihood-free inference for the ETAS model of earthquake occurrences
Unconstrained parametrizations for variance-covariance matrices
Best-subset instrumental variable selection method using mixed integer optimization with applications to health-related quality of life and education-wage analyses
Adaptive sufficient sparse clustering by controlling false discovery
Accurate ARL computation for EWMA-S 2 control charts
Markov chain importance sampling with applications to rare event probability estimation
Expectile and M-quantile regression for panel data
On shape detection in noisy images with particular reference to ultrasonography
The locally Gaussian density estimator for multivariate data
Maximum softly-penalized likelihood for mixed effects logistic regression
An adaptive scheduling scheme for calculating Bayes factors with thermodynamic integration using Simpson's rule
Computing marginal likelihoods via the Fourier integral theorem and pointwise estimation of posterior densities
Inference for ETAS models with non-Poissonian mainshock arrival times
On the use of cross-validation to assess performance in multivariate prediction
Stable local computation with conditional Gaussian distributions
Bootstrap estimation of the proportion of outliers in robust regression
Statistical inference for a single-index varying-coefficient model
Bayesian analysis for matrix-variate logistic regression with/without response misclassification
Detection of spatiotemporal changepoints: a generalised additive model approach
Bilinear modulation models for seasonal tables of counts
Constrained minimum energy designs
Core-elements for large-scale least squares estimation
Debiased transfer learning estimation and inference for multinomial regression
Testing mean independence with functional covariate
Optimal predictive partitioning
Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching
Importance sampling with the generalized exponential power density
Efficient EM-variational inference for nonparametric Hawkes process
Exploratory analysis of dynamic networks using latent functions
Parallel inference for massive distributed spatial data using low-rank models
Global sensitivity analysis of stochastic computer models with joint metamodels
Interpretable sparse SIR for functional data
A comprehensive comparison of goodness-of-fit tests for logistic regression models
Regularized bi-directional co-clustering
Rank aggregation using latent-scale distance-based models
Bayesian sparse covariance decomposition with a graphical structure
Quantitative convergence assessment for Markov chain Monte Carlo via cusums
Rank Test of Independence Based on Adaptive Hellinger Distance
Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes
Efficient estimation and correction of selection-induced bias with order statistics
Sequential Monte Carlo for rare event estimation
Robust estimation and wavelet thresholding in partially linear models
Importance sampling for rare event tracking within the ensemble Kalman filtering framework
Efficient estimation and model selection in large graphical models
Informed sub-sampling MCMC: approximate Bayesian inference for large datasets
Timing observations of diffusions
Learning variational autoencoders via MCMC speed measures
Optimal sampling times in bioequivalence studies using a simulated annealing algorithm
Statistical depth in abstract metric spaces
On predictive distributions and Bayesian networks
Gaussian variational approximation with sparse precision matrices
Wavelet Monte Carlo: a principle for sampling from complex distributions
Rate estimation in partially observed Markov jump processes with measurement errors
Simulation based composite likelihood
A permutation procedure for testing the equality of pattern hypotheses across groups involving correlation or covariance matrices
Differential Evolution Markov Chain with snooker updater and fewer chains
Particle Gibbs for likelihood-free inference of stochastic volatility models
Gröbner bases and factorisation in discrete probability and Bayes
Introduction to "Fast matrix computations for functional additive models" by S. Barthelmé
An adaptive sequential Monte Carlo method for approximate Bayesian computation
Fuzzy clustering with Barber modularity regularization
Resample-smoothing of Voronoi intensity estimators
Simultaneous confidence intervals by iteratively adjusted alpha for relative effects in the one-way layout
Density estimation with distribution element trees
Markov chain Monte Carlo with the Integrated Nested Laplace Approximation
Spatial autoregressive model for interval-valued data and applications
Optimal design of multifactor experiments via grid exploration
A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design
Generating directed networks with predetermined assortativity measures
Current tendencies in Free-Running Oscillators: a review
Composite likelihood methods for histogram-valued random variables
Air-HOLP: adaptive regularized feature screening for high dimensional correlated data
Some results concerning off-training-set and IID error for the Gibbs and the Bayes optimal generalizers
Bayesian sequential inference for nonlinear multivariate diffusions
Using mixtures in seemingly unrelated linear regression models with non-normal errors
Multilevel latent class models for cross-classified categorical data: model definition and estimation through stochastic EM
An EM-type algorithm for multivariate mixture models
Wrapped Gaussian Process Functional Regression Model for Batch Data on Riemannian Manifolds
Adaptive Reduced Multilevel Splitting
Ensemble Prediction via Covariate-dependent Stacking
Epidemiologic network inference
Integrative sparse reduced-rank regression via orthogonal rotation for analysis of high-dimensional multi-source data
Structured prior distributions for the covariance matrix in latent factor models
Fast accelerated failure time modeling for case-cohort data
Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach
A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems
Output-sensitive algorithms for Tukey depth and related problems
Bayesian inference of longitudinal count data with informative dropouts using a zero-inflated negative binomial mixed model
A comment on "A theoretical basis for perturbation methods" by Krishnamurty Muralidhar and Rathindra Sarathy
Predictive Subgroup Logistic Regression for Classification with Unobserved Heterogeneity
Quantile regression for longitudinal data based on latent Markov subject-specific parameters
Learning discrete decomposable graphical models via constraint optimization
Probabilistic time series forecasts with autoregressive transformation models
Bayesian learning via neural Schrödinger-Föllmer flows
Best linear estimation via minimization of relative mean squared error
A unified framework of constrained regression
Min-max crossover designs for two treatments binary and poisson crossover trials
Optimization of the generalized covariance estimator in noncausal processes
Computation of final outcome probabilities for the generalised stochastic epidemic
On the design of optimal change-over experiments through multi-objective simulated annealing
Bayesian model learning based on a parallel MCMC strategy
Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation
Stochastic boosting algorithms
Model Diagnostics for Remote Access Regression Servers
Max-min optimal discriminating designs for several statistical models
Bayesian parsimonious covariance estimation for hierarchical linear mixed models
Fast covariance estimation for sparse functional data
On predictive inference for intractable models via approximate Bayesian computation
Beyond homophilic dyadic interactions: the impact of network formation on individual outcomes
Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels
On perfect simulation for some mixtures of distributions
Letter to the Editor: on the use of improper priors for the shape parameters of asymmetric exponential power models
Bootstrap bias corrections for ensemble methods
Fast computation of spatially adaptive kernel estimates
Hilbert space methods for reduced-rank Gaussian process regression
High-dimensional local polynomial regression with variable selection and dimension reduction
Improving power by conditioning on less in post-selection inference for changepoints
Point process simulation of generalised inverse Gaussian processes and estimation of the Jaeger integral
Stochastic matching pursuit for Bayesian variable selection
Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials
Estimating non-simplified vine copulas using penalized splines
Filtering via approximate Bayesian computation
Strong convergence rates of probabilistic integrators for ordinary differential equations
Constructing two-level Q B -optimal screening designs using mixed-integer programming and heuristic algorithms
Cross-validation prior choice in Bayesian probit regression with many covariates
Variational Bayes inference for simultaneous autoregressive models with missing data
MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions
Improvements on scalable stochastic Bayesian inference methods for multivariate Hawkes process
Bayesian analysis of doubly semiparametric mixture cure models with interval-censored data
Multi-index antithetic stochastic gradient algorithm
Optimal Bayesian estimators for latent variable cluster models
Wavelet-based robust estimation and variable selection in nonparametric additive models
On sufficient dimension reduction methods based on a graphical model with non-concave penalty
de Finetti Priors using Markov chain Monte Carlo computations
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
Eliciting Dirichlet and Gaussian copula prior distributions for multinomial models
Graph matching beyond perfectly-overlapping Erdős-Rényi random graphs
Prediction of Chaotic Time Series in the Presence of Measurement Error: the Importance of Initial Conditions
Choice of wavelet smoothness, primary resolution and threshold in wavelet shrinkage
Exact posterior distributions and model selection criteria for multiple change-point detection problems
Composite likelihood inference for the Poisson log-normal model
Efficient simulation of p-tempered α-stable OU processes
Bandwidth selection in pre-smoothed particle filters
Parsimonious ultrametric Gaussian mixture models
Tractable Bayesian learning of tree belief networks
Algorithms for approximate conditional inference
Enhancing Quantile Function Estimation with Beta-Kernel Smoothing
Tuning tempered transitions
Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm
Unbalanced Longitudinal Data Clustering with a Copula Kernel Mixture Model
H-likelihood: problems and solutions
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Estimating parametric semi-Markov models from panel data using phase-type approximations
Mean and median bias reduction in generalized linear models
Tree-structured scale effects in binary and ordinal regression
Nonparametric empirical Bayes for the Dirichlet process mixture model
Model-free global likelihood subsampling for massive data
Explainable generalized additive neural networks with independent neural network training
Parallel tempering with equi-energy moves
Non-reversible Metropolis-Hastings
Penalized empirical likelihood estimation and EM algorithms for closed-population capture-recapture models
Nonparametric estimation for compound Poisson process via variational analysis on measures
Introduction to "Pre-processing for approximate Bayesian computation in image analysis" by M. Moores, C. Drovandi, K. Mengersen, C. Robert
On sequential Monte Carlo, partial rejection control and approximate Bayesian computation
Discussion of the paper by Dr. M. Müller
Hamiltonian Monte Carlo acceleration using surrogate functions with random bases
Slice sampling for simulation based fitting of spatial data models
Estimation of regime-switching diffusions via Fourier transforms
Sampling of pairs in pairwise likelihood estimation for latent variable models with categorical observed variables
On Bayesian lasso variable selection and the specification of the shrinkage parameter
On the generalization of the hazard rate twisting-based simulation approach
Fast and exact simulation of Gaussian random fields defined on the sphere cross time
Tensor train approximation of multivariate Gaussian density by scaling and squaring
An approximate fractional Gaussian noise model with $$\mathcal {O}(n)$$ O ( n ) computational cost
A nonparametric test of independence between the second-order structure of point patterns and covariates
Correction to: The COR criterion for optimal subset selection in distributed estimation
High-dimensional order-free multivariate spatial disease mapping
An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests
Parallel multivariate slice sampling
New mixed portmanteau tests for time series models
Clusterwise multivariate regression of mixed-type panel data
Particle-based energetic variational inference
Forward stability and model path selection
Multilevel particle filters for Lévy-driven stochastic differential equations
Communication-efficient transfer learning for adaptive Huber regression
Robust score matching for compositional data
Constrained monotone EM algorithms for mixtures of multivariate t distributions
Investigation about a screening step in model selection
Optimal non-negative forecast reconciliation
Efficient Markov chain Monte Carlo with incomplete multinomial data
Online Bayesian changepoint detection for network Poisson processes with community structure
Functional data analysis of generalized regression quantiles
Confidence regions for the stationary point of a quadratic response surface based on the asymptotic distribution of its MLE
Posterior inference on parameters of stochastic differential equations via non-linear Gaussian filtering and adaptive MCMC
Bayesian inference for Gaussian excursion set generated Cox processes with set-marking
Spatial model fitting for large datasets with applications to climate and microarray problems
Automatic model training under restrictive time constraints
Modal Clustering Using Semiparametric Mixtures and Mode Flattening
EM-based maximum likelihood parameter estimation for multivariate generalized hyperbolic distributions with fixed lambda
Correction to: Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm
Bayesian estimation of the latent dimension and communities in stochastic blockmodels
Subgraph nomination: query by example subgraph retrieval in networks
Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers
Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs
Stochastic cluster embedding
Distributed adaptive nearest neighbor classifier: algorithm and theory
Modeling of computer experiments for uncertainty propagation and sensitivity analysis
Extending mixtures of multivariate t -factor analyzers
On a class of σ-stable Poisson-Kingman models and an effective marginalized sampler
AR-ADASYN: angle radius-adaptive synthetic data generation approach for imbalanced learning
On Semi-Parametric Progressive Censoring
Convergence analysis of herded-Gibbs-type sampling algorithms: effects of weight sharing
Inference for cluster point processes with over- or under-dispersed cluster sizes
Moment-based density estimation of confidential micro-data: a computational statistics approach
Improving power of multivariate combination-based permutation tests
A stable estimator of the information matrix under EM for dependent data
Probabilistic time integration for semi-explicit PDAEs
General mixed Poisson regression models with varying dispersion
Multiple changepoint detection in categorical data streams
On the Fisher-Bingham distribution
Fitting Bayesian multiple random effects models
A projection pursuit index for large p small n data
Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis
Model-based clustering using copulas with applications
Generalized linear mixed joint model for longitudinal and survival outcomes
Fréchet distance-based cluster analysis for multi-dimensional functional data
Embedded topics in the stochastic block model
Adaptive sampling for Bayesian geospatial models
Multiplier bootstrap methods for conditional distributions
Fused lasso nearly-isotonic signal approximation in general dimensions
Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution
Book Review: Nonlinear Time Series: Theory, methods, and applications with R examples, by Randal Douc, Eric Moulines, David S. Stoffer - CRC Press, Boca Raton, FL, 2014, xx + 531 pp, $99.95 (H), ISBN 978-1-4665-0225-3
Nonparametric density deconvolution by weighted kernel estimators
Exact gradient evaluation for adaptive quadrature approximate marginal likelihood in mixed models for grouped data
Frequentist model averaging under a linear exponential loss
A.E. Gelfand, P.J. Diggle, M. Fuentes, P. Guttorp (eds.): Handbook of spatial statistics - Chapman & Hall/CRC, 2010
Computationally tractable approximate and smoothed Polya trees
Multivariate linear regression with non-normal errors: a solution based on mixture models
Sequential design of computer experiments for the estimation of a probability of failure
Robust Bayesian synthetic likelihood via a semi-parametric approach
Extensions of stability selection using subsamples of observations and covariates
Clustering of interval time series
Frequent problems in calculating integrals and optimizing objective functions: a case study in density deconvolution
A closed-form filter for binary time series
Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions
Comparative analysis of modern optimization tools for the p -median problem
Curve prediction and clustering with mixtures of Gaussian process functional regression models
Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications
Orthant probabilities of elliptical distributions from orthogonal projections to subspaces
On the Value of derivative evaluations and random walk suppression in Markov Chain Monte Carlo algorithms
Resolution dependence of the maximal information coefficient for noiseless relationship
Sequential model identification with reversible jump ensemble data assimilation method
Spilt conformal prediction with missing response
Detecting conflicting summary statistics in likelihood-free inference
Nonparametric maximum likelihood computation of a U-shaped hazard function
Efficient construction of Bayes optimal designs for stochastic process models
Analysis of generalized linear mixed models via a stochastic approximation algorithm with Markov chain Monte-Carlo method
Kent feature embedding for classification of compositional data with zeros
Decision-making under uncertainty: using MLMC for efficient estimation of EVPPI
A reduced-rank approach to predicting multiple binary responses through machine learning
Robust mean-shift clustering based on impartial trimming
A simple method for finding a legal configuration in complex Bayesian networks
Stable prediction in high-dimensional linear models
Insufficient Gibbs sampling
Random forest kernel for high-dimension low sample size classification
Monte Carlo Based Ensemble Forecasting
M-estimators of some GARCH-type models; computation and application
Distributional anchor regression
Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration
Exploring quasi Monte Carlo for marginal density approximation
A generalization of the adaptive rejection sampling algorithm
Likelihood-based inference for Tobit confirmatory factor analysis using the multivariate Student-t distribution
Source Data Perturbation and consistent sets of safe tables
Semi-parametric analysis of multi-rater data
Spatial modelling for binary data using␣a␣hidden conditional autoregressive Gaussian process: a multivariate extension of the probit model
A finite mixture model for image segmentation
Wavelet-based gradient boosting
Flexible tree-structured regression models for discrete event times
A stochastic approximation ECME algorithm to semi-parametric scale mixtures of centred skew normal regression models
The stochastic topic block model for the clustering of vertices in networks with textual edges
P-splines regression smoothing and difference type of penalty
Bayesian inference for pairwise interacting point processes
Hidden Markov Models with mixtures as emission distributions
LASSO for streaming data with adaptative filtering
A Conditional Density Approach to the Order Determination of Time Series
An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models
The forward-backward envelope for sampling with the overdamped Langevin algorithm
A test for the absence of aliasing or white noise in two-dimensional locally stationary wavelet processes
Interacting multiple try algorithms with different proposal distributions
A review of parallel processing for statistical computation
Scalable estimation strategies based on stochastic approximations: classical results and new insights
Weighted likelihood mixture modeling and model-based clustering
Modelling the role of variables in model-based cluster analysis
Extended differential geometric LARS for high-dimensional GLMs with general dispersion parameter
Kryging: geostatistical analysis of large-scale datasets using Krylov subspace methods
Spatially relaxed inference on high-dimensional linear models
Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors
Guest editorial: Introduction
Block-Additive Gaussian Processes under Monotonicity Constraints
A novel Hybrid RBF Neural Networks model as a forecaster
An adaptive spatial model for precipitation data from multiple satellites over large regions
Density deconvolution under a k-monotonicity constraint
Maximum likelihood from spatial random effects models via the stochastic approximation expectation maximization algorithm
Computational aspects of DNA mixture analysis - Exact inference using auxiliary variables in a Bayesian network
A quasi-Newton acceleration for high-dimensional optimization algorithms
Nudging the particle filter
Bayesian nonparametric spectral density estimation using B-spline priors
Transformed density rejection with inflection points
A coordinate descent algorithm for computing penalized smooth quantile regression
Distributed additive hazards regression analysis of multi-site current status data without using individual-level data
Parabolic acceleration of the EM algorithm
Robust finite mixture modeling of multivariate unrestricted skew-normal generalized hyperbolic distributions
A new mixture model on the simplex
Estimating mixed-effects differential equation models
On automatic bias reduction for extreme expectile estimation
On confidence intervals for semiparametric expectile regression
Reliable error estimation for Sobol' indices
Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms
A reweighting approach to robust clustering