Some nonparametric hybrid predictive models: asymptotic properties and applications - Chakraborty, Tanujit (Kolkata: Indian Statistical Institute, 2020)
Prediction problems like classification, regression, and time series forecasting have always attracted both the statisticians and computer scientists worldwide to take up the challenges of data science and implementation of complicated models using modern computing facilities. But most traditional statistical and machine learning models assume the available data to be well-behaved in terms of the presence of a full set of essential features, equal size of classes, and stationary data structures in all data instances, etc. Practical data sets from the domain of business analytics, process and quality control, software reliability, and macroeconomics, to name a few, suffer from various complexities and irregularities that are often sufficient to confuse any predictive model. This can degrade the ability of the learning models to learn from the data. Motivated by this, we develop some nonparametric hybrid predictive models and study their statistical properties for theoretical robustness in this thesis
Introduction -- Preliminaries -- A Nonparametric Hybrid Model for Pattern Classification -- Hellinger Net : A Hybrid Model for Imbalanced Learning -- A Distribution-free Hybrid Method for Regression Modeling -- Bayesian Neural Tree Models for Nonparametric Regression -- A Hybrid Time Series Model for Macroeconomic Forecasting -- Conclusions
Thesis (Ph.D.) - Indian Statistical Institute, 2020
Guided by Prof. Ashis Kumar Chakraborty
Keywords: Nonparametric Hybrid Model; Asymptotic Properties; Statistical Learning Model; Bayesian Addictive Regression Trees (BART); Classification & Regression Trees (CART)