In a classification problem, before building prediction model, it is very important to identify informative features rather than using tens or thousands which may penalize some learning methods and increase the risk of over-fitting. To overcome these problems, best solution use feature selection. this article, we propose new filter method for selection, by combining Relief algorithm multi-crite...

In this paper, we propose a class of monitoring statistics for mean shift in sequence high-dimensional observations. Inspired by the recent U-statistic based retrospective tests developed Wang et al.(2019) and Zhang al.(2020), advance approach to sequential problem developing new adaptive procedure that can detect both dense sparse changes real-time. Unlike where self-normalization was used the...

As the climate change is likely to be adversely affecting yield of paddy production, thence it has brought a limelight probable challenges on human particularly regional food security issues. This paper aims fit multivariate time series production variables using copula functions and predicts next year event based data five countries in southeast Asia. In particular, most appropriate marginal d...

In recent years, data dimensionality has increasingly become a concern, leading to many parameter and dimension reduction techniques being proposed in the literature. A parameter-wise co-clustering model, for (possibly high-dimensional) modelled via continuous random variables, is presented. The although allowing more flexibility, still maintains very high degree of parsimony interpretability a...