# Tathagata BasuUniversity of Strathclyde · Department of Civil and Environmental Engineering

Tathagata Basu

Doctor of Philosophy

## About

26

Publications

3,168

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14

Citations

Introduction

Additional affiliations

October 2021 - December 2022

Education

November 2017 - March 2021

July 2015 - May 2017

July 2012 - May 2015

## Publications

Publications (26)

Drone Logistic Network is an emerging technology in the sector of logistic support with potential applications in goods delivery, postal shipping, healthcare networks, emergency response, etc. This highly complex system often involves different design requirements along with multiple objective functions such as time for delivery, capital and operat...

Causal inference concerns finding the treatment effect on subjects along with causal links between the variables and the outcome. However, the underlying heterogeneity between subjects makes the problem practically unsolvable. Additionally, we often need to find a subset of explanatory variables to understand the treatment effect. Currently, variab...

Causal inference using observational data is an important aspect in many fields such as epidemiology, social science, economics, etc. In particular, our goal is to find the treatment effect on the subjects along with the causal links between the variables and the outcome. However, estimation for such problems are extremely difficult as the treatmen...

Drone Logistic Network (or simply, DLN) is an emerging topic in the sector of transportation networks with applications in goods delivery, postal shipping, healthcare networks etc. It is a rather complex system which have different types of drones and ground facilities and it also requires a robust design of the network to ensure optimal time for d...

Regression analysis with missing data is a regular occurrent in the field of statistical modelling. In the precise setting, single value imputation strategy is used to get rid of the missing data. However, such methods rely on several observational assumptions. A simple but robust workaround is to consider interval (valued) imputation. This allows...

Learning to rank is an important problem in many sectors ranging from social sciences to artificial intelligence. However, it remains a rather difficult task to perform. Therefore, in some cases, it is preferable to perform cautious inference. For this purpose, we look into the possibility of an imprecise probabilistic approach for the Plackett-Luc...

We propose a cautious Bayesian variable selection routine by investigating the sensitivity of a hierarchical model, where the regression coefficients are specified by spike and slab priors. We exploit the use of latent variables to understand the importance of the co-variates. These latent variables also allow us to obtain the size of the model spa...

Learning to rank is an important problem in many sectors ranging from social sciences to artificial intelligence. However, it remains a rather difficult task to perform. Therefore, in some cases, it is preferable to perform cautious inference. For this purpose, we look into the possibility of an imprecise probabilistic approach for the Plackett-Luc...

We propose a cautious Bayesian variable selection routine by investigating the sensitivity of a hierarchical model, where the regression coefficients are specified by spike and slab priors. We exploit the use of latent variables to understand the importance of the co-variates. These latent variables also allow us to obtain the size of the model spa...

Learning to rank has become an important part in the fields of machine learning and statistical learning. Rankings are indeed present in many applications, including cognitive psychology, recommender systems , sports tournament or automated algorithm choices. Rankings are however prone to subjectivity (when provided by users) and to incompleteness...

Learning to rank has become an important part in the fields of machine learning and statistical learning. Rankings are indeed present in many applications, including cognitive psychology, recommender systems, sports tournament or automated algorithm choices. Rankings are however prone to subjectivity (when provided by users) and to incompleteness (...

We discuss the possibility of a robust Bayesian approach for estimating causal effects in observational studies. We are interested in estimating the causal effect and the association between the inputs and the responses. Therefore, we tackle this problem using regression models. We obtain the regression coefficients through a robust Bayesian analys...

We propose a novel robust Bayesian variable selection routine using a hierarchical model, which specifies the regression coefficients using spike and slab priors. We exploit the use of latent variables to understand the importance of the co-variates. We adopt a robust Bayesian approach to specify the selection probabilities of these latent variable...

Bayesian variable selection is one of the popular topics in modern day statistics. It is an important tool for high dimensional statistics, where the number of model parameters is greater than the number of observations. Several Bayesian models have been proposed for variable selection. However, a convincing robust Bayesian approach is yet to be in...

Regularization techniques, which sit at the interface of statistical modeling and machine learning, are often used in the engineering or other applied sciences to tackle high dimensional regression (type) problems. While a number of regularization methods are commonly used, the ‘Least Absolute Shrinkage and Selection Operator’ or simply LASSO is po...

Since uncertainty is persistent in engineering analyses, this chapter aimed to introduce methods to describe and reason with under uncertainty in various scenarios. Probability theory is the most widely used methodology for uncertainty quantification for a long time and has proven to be a powerful tool for this task. Nevertheless, the construction...

Binary classification is a well known problem in statistics. Besides classical methods, several techniques such as the naive credal classifier (for categorical data) and imprecise logistic regression (for continuous data) have been proposed to handle sparse data. However, a convincing approach to the classification problem in high dimensional probl...

Binary classification is a well known problem in statistics. Besides classical methods , several techniques such as the naive credal classifier (for categorical data) and imprecise logistic regression (for continuous data) have been proposed to handle sparse data. However, a convincing approach to the classification problem in very high dimensional...

Sparse regression is an efficient statistical modelling technique which is of major relevance for high dimensional statistics. There are several ways of achieving sparse regression, the well-known lasso being one of them. However, lasso variable selection may not be consistent in selecting the true sparse model. Zou (2006) proposed an adaptive form...

In this paper we prove a new probability inequality that can be used to construct p-boxes in a non-parametric fashion, using the sample mean and sample standard deviation instead of the true mean and true standard deviation. The inequality relies only on exchangeability and boundedness.