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Full-Text Articles in Statistics and Probability
A Review Of Statistical Learning Methods With Applications, Natalie R. Masse
A Review Of Statistical Learning Methods With Applications, Natalie R. Masse
Major Papers
Statistical learning refers to a set of tools for modelling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. This paper aims to outline some of the key statistical learning methods in the areas of prediction and classification of data. The goal is to discuss the theory and methodology of Ordinary Least Squares Regression, Ridge Regression, Lasso Regression, Logistic Regression, K-Nearest Neighbours method of classification, Linear and Quadratic Discriminant analysis, and Classification Trees. We then discuss the idea of Cross Validation, and demonstrate these …
To Logit Or Not To Logit Data In The Unit Interval: A Simulation Study, Kayode Idris Hamzat
To Logit Or Not To Logit Data In The Unit Interval: A Simulation Study, Kayode Idris Hamzat
Major Papers
In this paper, we recommend a mechanism for determining whether to logit or not to logit data in the unit interval which is based on quantile estimation of data between 0 and 1. By using a simulated dataset generated from a Beta regression model, the estimated quantile for this model perform better than those based on the linear quantile regression with logit transformation.
Further, we investigate the performance of the quantile regression estimators based on the LQR and we conclude that it is better than those based on the Beta regression when the distribution is contaminated with 10% uniform numbers …