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Articles 1 - 3 of 3
Full-Text Articles in Statistical Models
Best Probable Subset: A New Method For Reducing Data Dimensionality In Linear Regression, Elieser Nodarse
Best Probable Subset: A New Method For Reducing Data Dimensionality In Linear Regression, Elieser Nodarse
FIU Electronic Theses and Dissertations
Regression is a statistical technique for modeling the relationship between a dependent variable Y and two or more predictor variables, also known as regressors. In the broad field of regression, there exists a special case in which the relationship between the dependent variable and the regressor(s) is linear. This is known as linear regression.
The purpose of this paper is to create a useful method that effectively selects a subset of regressors when dealing with high dimensional data and/or collinearity in linear regression. As the name depicts it, high dimensional data occurs when the number of predictor variables is far …
Computational Analysis Of Large-Scale Trends And Dynamics In Eukaryotic Protein Family Evolution, Joseph Boehm Ahrens
Computational Analysis Of Large-Scale Trends And Dynamics In Eukaryotic Protein Family Evolution, Joseph Boehm Ahrens
FIU Electronic Theses and Dissertations
The myriad protein-coding genes found in present-day eukaryotes arose from a combination of speciation and gene duplication events, spanning more than one billion years of evolution. Notably, as these proteins evolved, the individual residues at each site in their amino acid sequences were replaced at markedly different rates. The relationship between protein structure, protein function, and site-specific rates of amino acid replacement is a topic of ongoing research. Additionally, there is much interest in the different evolutionary constraints imposed on sequences related by speciation (orthologs) versus sequences related by gene duplication (paralogs). A principal aim of this dissertation is to …
Context-Aware Personalized Point-Of-Interest Recommendation System, Ramesh Raj Baral
Context-Aware Personalized Point-Of-Interest Recommendation System, Ramesh Raj Baral
FIU Electronic Theses and Dissertations
The increasing volume of information has created overwhelming challenges to extract the relevant items manually. Fortunately, the online systems, such as e-commerce (e.g., Amazon), location-based social networks (LBSNs) (e.g., Facebook) among many others have the ability to track end users' browsing and consumption experiences. Such explicit experiences (e.g., ratings) and many implicit contexts (e.g., social, spatial, temporal, and categorical) are useful in preference elicitation and recommendation. As an emerging branch of information filtering, the recommendation systems are already popular in many domains, such as movies (e.g., YouTube), music (e.g., Pandora), and Point-of-Interest (POI) (e.g., Yelp).
The POI domain has many …