Recent years have seen the developments of several methods for sparse principal component analysis due to its importance in the analysis of high dimensional data. Despite the demonstration of their usefulness in practical applications, they are limited in terms of lack of orthogonality in the...

The decades-long history of friendly relationship in both countries bore the fruits in the form of Pakistan-China free-trade agreement (PCFTA) in 2006. This paper aims to estimate Pakistan's bilateral trade potential with China by employing gravity model of trade in a panel data set covering the...

Amplified Fragment Length Polymorphism (AFLP) markers are formed by selective amplification of DNA fragments from digested total genomic DNA. The technique is popular because it is a relatively inexpensive way to produce large numbers of reproducible genetic markers. In this paper, we describe a...

Recommender systems have been widely used by online stores to suggest items of interest to users. These systems often identify a subset of items from a much larger set that best matches the user's interest. A key concern with existing approaches is over-specialization, which results in returning...

Principal component analysis (PCA) is one of the key techniques in functional data analysis. One important feature of functional PCA is that there is a need for smoothing or regularizing of the estimated principal component curves. Silverman's method for smoothed functional principal component...