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This work focuses on dimension-reduction techniques for modelling conditional extreme values. Specifically, we investigate the idea that extreme values of a response variable can be explained by nonlinear functions derived from linear projections ...

I show analytically that the average of k bootstrapped correlation matrices rapidly becomes positive-definite as k increases, which provides a simple approach to regularize singular Pearson correlation matrices. If n is the order of the matrix ...

Longitudinal data consists of repeated observations that are typically correlated, which makes the likelihood-based inference challenging. This limits the use of Bayesian methods for longitudinal data in many general situations. To ...

Many modern statistical models are used for both insight and prediction when applied to data. When models are used for prediction one should optimise parameters through a prediction error loss function. Estimation methods based on multiple steps ...

A new computational framework is developed in this paper for investigating the time-dependent behaviour of concrete including creep, shrinkage and cracking. The developed model aims to explain certain aspects of the time-dependent cracking and ...

Choosing a shrinkage method can be done by selecting a penalty from a list of pre-specified penalties or by constructing a penalty based on the data. If a list of penalties for a class of linear models is given, we introduce a predictive stability ...

Proper function of a wastewater treatment plant (WWTP) relies on maintaining a delicate balance between a multitude of competing microorganisms. Gaining a detailed understanding of the complex network of interactions therein is essential to ...

• Vector autoregressive models can detect interactions in microbial communities.
• Regularised horseshoe performs well as a prior for a sparse autoregressive matrix.
• Including environmental factors improves inference of microbial ...

Tuning of model-based boosting algorithms relies mainly on the number of iterations, while the step-length is fixed at a predefined value. For complex models with several predictors such as Generalized additive models for location, scale and shape ...

Few-shot learning performs classification tasks and regression tasks on scarce samples. As one of the most representative few-shot learning models, Prototypical Network represents each class as sample average, or a prototype, and measures the ...

With a focus on linear models with smooth functional covariates, we propose a penalization framework (SACR) based on the nonzero centered ridge, where the center of the penalty is adaptively reweighted, starting from the ordinary ridge ...

This paper discusses simultaneous parameter estimation and variable selection and presents a new penalized regression method. The method is based on the idea that the coefficient estimates are shrunken towards a predetermined coefficient vector ...

This paper presents the R package PlackettLuce, which implements a generalization of the Plackett–Luce model for rankings data. The generalization accommodates both ties (of arbitrary order) and partial rankings (complete rankings of subsets of ...

This paper is devoted to the multivariate estimation of a vector of Poisson means. A novel loss function that penalises bad estimates of each of the parameters and also the sum (or equivalently the mean) of the parameters is ...

A Boolean function f : { 0 , 1 } n → { 0 , 1 } is weighted symmetric if there exist a function g : Z → { 0 , 1 } and integers w 0 , w 1 , … , w n such that f ( x 1 , … , x n ) = g ( w 0 + ∑ i = 1 n w i x i ) holds. In this paper, we ...

The paper proposes a cross-validated linear shrinkage estimation for population covariance matrices. Moreover we also propose a novel weighted estimator based on the thresholding and shrinkage methods for high dimensional datasets. It ...

Bayesian global-local shrinkage estimation with the generalized horseshoe prior represents the state-of-the-art for Gaussian regression models. The extension to non-Gaussian data, such as binary or Student-t regression, is usually done by ...

The standard approach for prediction of multiple correlated outcome measures overpredicts the unknown observation in the linear model setup if associated covariate measures follow a certain distribution. It is desired to have a ...

• Developing an image processing algorithm for segmenting and separating the center potato slice from other slices.

In this study, machine vision was used for measuring area shrinkage of potato slices during thin layer drying process and then an artificial neural network (ANN) and linear models were investigated to predict moisture content (MC) of ...

One powerful theme in complexity theory and pseudorandomness in the past few decades has been the use of lower bounds to give pseudorandom generators (PRGs). However, the general results using this hardness vs. randomness paradigm suffer from a ...

To solve the conflicting requirement of fast convergence and low steady-state misalignment, a variable step-size shrinkage set-membership affine projection algorithm is proposed, which is efficient for the correlated input signal and noisy input ...