Date of Submission


Date of Award


Institute Name (Publisher)

Indian Statistical Institute

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Subject Name



SQC and OR Unit (Kolkata)


Chakraborty, Ashis Kumar (SQCOR-Kolkata; ISI)

Abstract (Summary of the Work)

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. In this chapter, we provide the genesis of predictive models and the history of the hybrid and ensemble models. Subsequently, we discuss the occurrences of the different data complexities and irregularities, such as feature selection, class imbalance, regression estimation, and nonstationarity. Finally, the chapter ends with an enumeration of the contributions made herein, in an attempt to design novel solution strategies for these application-driven statistical problems.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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Mathematics Commons