Browsing by Author "Anjaneyulu, GVSR"

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    On the use of Sparse Principal Component Analysis and Robust: Selection Features of Maize Yield in Rural Tanzania
    (MANECH Pblications, 2017) Mbukwa, Justine N.; Anjaneyulu, GVSR
    This paper has been motivated as a result of an existence of high dimensionality problem in maize yield. This means that an application of the Sparse Principal Component Analysis (SPCA) pattern recognition technique is unknown in selecting few consistent features and easier interpretation as opposed to classical PCA. This paper fulfills the existing knowledge gap in the context of Tanzania. A structure questionnaire was used to collect primary data from Mbozi and Mvomero Districts among small farming household in rural areas. The study was designed on the basis of hierarchical random sampling. The breakdown of facts was made by R-Statistical computing (version 3.3.2) whereas the findings were depicted using graphs and tables. The statistical estimates like percentage, mean and variance were also used. In line with SPCA, PCA and Robust PCA were also fitted for comparison purpose. Results showed 19 variables were condensed to six components explaining 63.7 per cent variations under PCA. Contrary to these findings, there were great improvements of the loadings, consistent and easier to interpret in each PC of the modified model (SPCA). However, the paper discovered that the Robust PCA condensed the p-variable to two PCs such that PC1 explained (81.0 per cent) variances. The study recommends the Sparse and Robustness as the best filtering techniques with reliable results as contrasted to the ordinary PCA.
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    Statistical Analytic Tool for Dimensionalities in Big Data: The role of Hierarchical Cluster Analysis
    (Research India Publications, 2016) Mbukwa, Justine N.; Tabita, G Neeha; Anjaneyulu, GVSR; Rajasekharam, OV
    An interest for presenting this paper rose because of massive increase information with a very high dimensional from different sources in this era of globalization. Data are produced continuously and are unstructured (1). This paper is confined to literature review search for big data issue and challenges of several scopes in data. It brings a detailed discussion on the problem on these data and analysis done using the effective multivariate statistical tool namely clustering analysis technique as a data reduction technique. It is used as a base for discussion for existing challenge of multi-dimensionalities of data. The findings indicated that, the world is noisy due to massive flow of information continuously. Findings revealed that data emanating from face book, you tube and twitter can be used to predict the epidemic of influenza and even market trend (2 and 3). With face book data is used to predict the people`s interest. However, data from different sources have been proved to be useful in decision making efficiently and effectively for public as well as private sector. Cluster analysis technique sorts data/alike things into groups, to see if there a high natural degree association among members of the same group and low between members of different groups. Finally, this technique has proved failure to handle such heap of data with varied sources. With regards to data stored, it remains to be a challenge in terms of analysis among researchers and scientists. Therefore, it calls for advanced statistical software to cater for such an existing challenges.