Sparse principal components analysis (SPCA) is a technique for finding principal components with a small number of non-zero loadings. Our contribution to this methodology is twofold. First we derive the sparse solutions that minimise the least squares criterion subject to sparsity requirements. Second, recognising that sparsity is not the only requirement for achieving simplicity, we suggest a backward elimination algorithm that computes sparse solutions with large loadings. This algorithm can be run without specifying the number of non-zero loadings in advance. It is also possible to impose the requirement that a minimum amount of variance be explained by the components. We give thorough comparisons with existing SPCA methods and present several examples using real datasets.

We propose a new sparse principal component analysis (SPCA) method in which the solutions are obtained by projecting the full cardinality principal components onto subsets of variables. The resulting components are guaranteed to explain a given proportion of variance. The computation of these solutions is very efficient. The proposed method compares well with the optimal least squares sparse components. We show that other SPCA methods fail to identify the best sparse approximations of the principal components and explain less variance than our solutions. We illustrate and compare our method with the analysis of a real dataset containing socioeconomic data and the computational results for nine datasets of increasing dimension with up to 16,000 variables.

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Asset indices have been used since the late 1990s to measure wealth in developing countries. We extend the standard methodology for estimating asset indices using principal component analysis in two ways: by introducing constraints that force the indices to have increasing value as the number of assets owned increases, and by estimating sparse indices with a few key assets. This is achieved by combining categorical and sparse principal component analysis. We also apply this methodology to the estimation of per capita level asset indices. Using household survey data from northwest Vietnam and northeast Laos, we show that the resulting asset indices improve the prediction and ranking of income both at household and per capita level.

Due to the penetration of smartphones and associated mobile devices, mobile gaming has become a ubiquitous industry worldwide. Players now have access to games at all times. Extending previous research and the Uses and Gratifications approach this paper presents an alternative conceptual model that can offer explanations towards understanding why players play the mobile game they play most frequently.