Statistics and Computing
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Discussion on the paper by Friedman and Fisher
Statistics and Computing - Tập 9 - Trang 146-147 - 1999
Enmsp: an elastic-net multi-step screening procedure for high-dimensional regression
Statistics and Computing - Tập 34 - Trang 1-16 - 2024
To improve the estimation efficiency of high-dimensional regression problems, penalized regularization is routinely used. However, accurately estimating the model remains challenging, particularly in the presence of correlated effects, wherein irrelevant covariates exhibit strong correlation with relevant ones. This situation, referred to as correlated data, poses additional complexities for model...... hiện toàn bộ
Distributed adaptive nearest neighbor classifier: algorithm and theory
Statistics and Computing - Tập 33 - Trang 1-23 - 2023
When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification. We propose a novel distributed adaptive NN classifier for which the number of nearest neighbors is a tuning parameter stochastically chosen by a data-driven criterion. An early stopping rule is proposed when searching ...... hiện toàn bộ
Resample-smoothing of Voronoi intensity estimators
Statistics and Computing - Tập 29 Số 5 - Trang 995-1010 - 2019
Efficient Bayesian estimation of the multivariate Double Chain Markov Model
Statistics and Computing - Tập 23 - Trang 467-480 - 2012
The Double Chain Markov Model (DCMM) is used to model an observable process
$Y = \{Y_{t}\}_{t=1}^{T}$
as a Markov chain with transition matrix,
$P_{x_{t}}$
, dependent on the value of an unobservable (hidden) Ma...... hiện toàn bộ
Distributed statistical optimization for non-randomly stored big data with application to penalized learning
Statistics and Computing - Tập 33 - Trang 1-13 - 2023
Distributed optimization for big data has recently attracted enormous attention. However, the existing algorithms are all based on one critical randomness condition, i.e., the big data are randomly distributed on different machines. This is seldom in practice, and violating this condition can seriously degrade the estimation accuracy. To fix this problem, we propose a pilot dataset surrogate loss ...... hiện toàn bộ
Generalised likelihood profiles for models with intractable likelihoods
Statistics and Computing - Tập 34 - Trang 1-14 - 2023
Likelihood profiling is an efficient and powerful frequentist approach for parameter estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately, these methods cannot be easily applied for stochastic models without a tractable likelihood function. Such models are typical in many fields of science, rendering these classical approaches impractical in these settings. T...... hiện toàn bộ
Calibrating the Gaussian multi-target tracking model
Statistics and Computing - Tập 25 - Trang 595-608 - 2014
We present novel batch and online (sequential) versions of the expectation–maximisation (EM) algorithm for inferring the static parameters of a multiple target tracking (MTT) model. Online EM is of particular interest as it is a more practical method for long data sets since in batch EM, or a full Bayesian approach, a complete browse of the data is required between successive parameter updates. On...... hiện toàn bộ
Control variates for stochastic gradient MCMC
Statistics and Computing - - 2018
It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size. A popular class of methods for solving this issue is stochastic gradient MCMC (SGMCMC). These methods use a noisy estimate of the gradient of the log-posterior, which reduces the per iteration computational cost of the algorithm. Despite this, there are a number of results suggesting that stochastic gradi...... hiện toàn bộ
The minimum regularized covariance determinant estimator
Statistics and Computing - Tập 30 - Trang 113-128 - 2019
The minimum covariance determinant (MCD) approach estimates the location and scatter matrix using the subset of given size with lowest sample covariance determinant. Its main drawback is that it cannot be applied when the dimension exceeds the subset size. We propose the minimum regularized covariance determinant (MRCD) approach, which differs from the MCD in that the scatter matrix is a convex co...... hiện toàn bộ
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