Soudeep Deb
YOU?
Author Swipe
View article: E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting
E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting Open
Modeling and forecasting air quality is crucial for effective air pollution management and protecting public health. Air quality data, characterized by nonlinearity, nonstationarity, and spatiotemporal correlations, often include extreme p…
View article: Nonparametric method of structural break detection in stochastic time series regression model
Nonparametric method of structural break detection in stochastic time series regression model Open
We propose a nonparametric algorithm to detect structural breaks in the conditional mean and/or variance of a time series. Our method does not assume any specific parametric form for the dependence structure of the regressor, the time seri…
View article: Nonparametric quantile regression for spatio-temporal processes
Nonparametric quantile regression for spatio-temporal processes Open
In this paper, we develop a new and effective approach to nonparametric quantile regression that accommodates ultrahigh-dimensional data arising from spatio-temporal processes. This approach proves advantageous in staving off computational…
View article: What elements of the opening set influence the outcome of a tennis match? An in-depth analysis of Wimbledon data
What elements of the opening set influence the outcome of a tennis match? An in-depth analysis of Wimbledon data Open
This study examines the importance of game elements of the first set in Wimbledon matches in deciding the match outcome. We propose a LASSO-induced logistic regression model based on first set data to identify the variables that impact the…
View article: Forecasting elections from partial information using a Bayesian model for a multinomial sequence of data
Forecasting elections from partial information using a Bayesian model for a multinomial sequence of data Open
Predicting the winner of an election is of importance to multiple stakeholders. To formulate the problem, we consider an independent sequence of categorical data with a finite number of possible outcomes in each. The data is assumed to be …
View article: Real-time forecasting within soccer matches through a Bayesian lens
Real-time forecasting within soccer matches through a Bayesian lens Open
This article employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilise a multinomial probit regression in a novel framework to estimate th…
View article: A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States
A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States Open
A new clustering algorithm for spatio-temporal data is developed. The proposed method leverages a weighted combination of a spatial haversine distance matrix and a spectral-density based temporal distance matrix between the locations. Conc…
View article: A Bayesian approach to identify changepoints in spatio-temporal ordered categorical data: An application to COVID-19 data
A Bayesian approach to identify changepoints in spatio-temporal ordered categorical data: An application to COVID-19 data Open
Although there is substantial literature on identifying structural changes for continuous spatio-temporal processes, the same is not true for categorical spatio-temporal data. This work bridges that gap and proposes a novel spatio-temporal…
View article: Optimal selection of the starting lineup for a football team
Optimal selection of the starting lineup for a football team Open
The success of a football team depends on various individual skills and performances of the selected players as well as how cohesively they perform. We propose a two-stage process for selecting optimal playing eleven of a football team fro…
View article: Impact of COVID-19 on public social life and mental health: a statistical study of google trends data from the USA
Impact of COVID-19 on public social life and mental health: a statistical study of google trends data from the USA Open
The COVID-19 pandemic has caused a significant disruption in the social lives and mental health of people across the world. This study aims to asses the effect of using internet search volume data. We categorize the widely searched keyword…
View article: Effect of influence in voter models and its application in detecting significant interference in political elections
Effect of influence in voter models and its application in detecting significant interference in political elections Open
In this article, we study the effect of vector-valued interventions in votes under a binary voter model, where each voter expresses their vote as a $0-1$ valued random variable to choose between two candidates. We assume that the outcome i…
View article: A review and recommendations on variable selection methods in regression models for binary data
A review and recommendations on variable selection methods in regression models for binary data Open
The selection of essential variables in logistic regression is vital because of its extensive use in medical studies, finance, economics and related fields. In this paper, we explore four main typologies (test-based, penalty-based, screeni…
View article: A spatio-temporal statistical model to analyze COVID-19 spread in the USA
A spatio-temporal statistical model to analyze COVID-19 spread in the USA Open
Coronavirus pandemic has affected the whole world extensively and it is of immense importance to understand how the disease is spreading. In this work, we provide evidence of spatial dependence in the pandemic data and accordingly develop …
View article: Nonparametric quantile regression for time series with replicated observations and its application to climate data
Nonparametric quantile regression for time series with replicated observations and its application to climate data Open
This paper proposes a model-free nonparametric estimator of conditional quantile of a time series regression model where the covariate vector is repeated many times for different values of the response. This type of data is abound in clima…
View article: Modeling a sequence of multinomial data with randomly varying probabilities
Modeling a sequence of multinomial data with randomly varying probabilities Open
We consider a sequence of variables having multinomial distribution with the number of trials corresponding to these variables being large and possibly different. The multinomial probabilities of the categories are assumed to vary randomly…
View article: A mathematical take on the competitive balance of a football league
A mathematical take on the competitive balance of a football league Open
Competitive balance in a football league is extremely important from the perspective of economic growth of the industry. Many researchers have earlier proposed different measures of competitive balance, which are primarily adapted from the…
View article: Analyzing airlines stock price volatility during <scp>COVID</scp>‐19 pandemic through internet search data
Analyzing airlines stock price volatility during <span>COVID</span>‐19 pandemic through internet search data Open
Recent Coronavirus pandemic has prompted many regulations which are affecting the stock market. Especially because of lockdown policies across the world, the airlines industry is suffering. We analyse the stock price movements of three maj…
View article: Prevalence and spectrum of diabetic peripheral neuropathy and its correlation with insulin resistance - An experience from eastern India
Prevalence and spectrum of diabetic peripheral neuropathy and its correlation with insulin resistance - An experience from eastern India Open
AIMS Diabetes mellitus is a public health problem worldwide, with diabetic neuropathy (DN) being a common complication. Studies indicate that, neurons can develop insulin resistance (IR) and cannot respond to the neurotrophic properties of…
View article: Analyzing count data using a time series model with an exponentially decaying covariance structure
Analyzing count data using a time series model with an exponentially decaying covariance structure Open
Count data appears in various disciplines. In this work, a new method to analyze time series count data has been proposed. The method assumes exponentially decaying covariance structure, a special class of the Matérn covariance function, f…
View article: Forecasting count data using time series model with exponentially decaying covariance structure
Forecasting count data using time series model with exponentially decaying covariance structure Open
Count data appears in various disciplines. In this work, a new method to analyze time series count data has been proposed. The method assumes exponentially decaying covariance structure, a special class of the Matern covariance function, f…
View article: A time series method to analyze incidence pattern and estimate reproduction number of COVID-19
A time series method to analyze incidence pattern and estimate reproduction number of COVID-19 Open
The ongoing pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China in the end of 2019. It has already affected more than 300,000 people, with the number of deaths nearing 13000 across the world. As it has been posing a huge thr…
View article: Spatial modeling of shot conversion in soccer to single out goalscoring ability
Spatial modeling of shot conversion in soccer to single out goalscoring ability Open
Goals are results of pin-point shots and it is a pivotal decision in soccer when, how and where to shoot. The main contribution of this study is two-fold. First, we show that there exists high spatial correlation in the data of shots acros…
View article: Family Firearm Ownership and Firearm-Related Mortality Among Young Children: 1976–2016
Family Firearm Ownership and Firearm-Related Mortality Among Young Children: 1976–2016 Open
BACKGROUND: Firearm-related fatalities are a top 3 cause of death among children in the United States. Despite historical declines in firearm ownership, the firearm-related mortality rate among young children has risen over the past decade…
View article: An asymptotic theory for spectral analysis of random fields
An asymptotic theory for spectral analysis of random fields Open
For a general class of stationary random fields we study asymptotic properties of the discrete Fourier transform (DFT), periodogram, parametric and nonparametric spectral density estimators under an easily verifiable short-range dependence…