Trending Research
A scalable estimate of the extra-sample prediction error via approximate leave-one-out
Motivated by the low bias of the leave-one-out cross validation (LO) method, we propose a computationally efficient closed-form approximate leave-one-out formula (ALO) for a large class of regularized estimators.
Methodology
Modeling microbial abundances and dysbiosis with beta-binomial regression
Using a sample from a population to estimate the proportion of the population with a certain category label is a broadly important problem.
Methodology
KFAS: Exponential Family State Space Models in R
State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data.
Computation Methodology
Multiple Target, Multiple Type Filtering in RFS Framework
First, we extend the standard Probability Hypothesis Density (PHD) filter for multiple type of targets, each with distinct detection properties, to develop multiple target, multiple type filtering, N-type PHD filter, where $N\geq2$, for handling confusions among target types.
Applications
Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption
Using ABC, which depends on many simulations from the considered model, we develop an inferential framework to learn parameters of a stochastic numerical simulator of volcanic eruption.
Computation Applications
Importance subsampling for power system planning under multi-year demand and weather uncertainty
This paper introduces a generalised version of importance subsampling for time series reduction/aggregation in optimisation-based power system planning models.
Applications
Marginal Likelihoods from Monte Carlo Markov Chains
In this paper, we present a method for computing the marginal likelihood, also known as the model likelihood or Bayesian evidence, from Markov Chain Monte Carlo (MCMC), or other sampled posterior distributions.
Computation Cosmology and Nongalactic Astrophysics
Propensity Score Matching and Subclassification in Observational Studies with Multi-level Treatments
In this paper, we develop new methods for estimating average treatment effects in observational studies, focusing on settings with more than two treatment levels under unconfoundedness given pre-treatment variables.
Methodology
The Holdout Randomization Test for Feature Selection in Black Box Models
tansey/hrt
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We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.
Methodology
Two-Sample Test with Copula Entropy
In this paper we propose a two-sample test based on copula entropy (CE).
Methodology
