Machine condition prognosis is an important part of the decision-making in condition-based
maintenance. By predicting the degradation of working conditions of machinery, it can …

Artificial neural network (ANN) is a popular data-driven modelling technique that has found
application in river flow forecasting over the last two decades. This can be attributed to its …

Statistical Theories and Methods with Applications to Economics and Business highlights
recent advances in statistical theory and methods that benefit econometric practice. It deals …

Least Angle Regression (LARS) is a variable selection method with proven performance for
cross-sectional data. In this paper, it is extended to time series forecasting with many …

Clustering longitudinal data for growth curve modelling is considered in this paper, where
we aim to optimally estimate the underpinning unknown group partition matrix. Instead of …

A novel model selection and averaging approach is proposed—through integrating the
corrected Akaike information criterion (AICc), the Gibbs sampler, and the Poisson regression …

After the outbreak of avian influenza and swine influenza, people realize that it is very
important to surveil the infectious diseases outbreak of livestock and poultry in flocks. Owing …

Finding associations between genetic markers and a phenotypic trait such as coronary
artery disease (CAD) is of primary interest in genome-wide association studies (GWAS). A …

This paper presents a novel ensemble model of artificial neural networks for rainfall forecast
incorporating dynamic variable selection. In the first phase of the model, meteorological …

Vector autoregressive (VAR) models are important and useful for modelling multivariate time
series. An appropriate VAR model is often required for such modelling for given data, for …