17 citations · 30 across the 23 of their papers we have counts for
12 papers · 1 filter
Long-term prediction intervals with many covariates
Sayar Karmakar, Marek Chudy, Wei Biao Wu
Accurate forecasting is one of the fundamental focus in the literature of econometric time-series. Often practitioners and policy makers want to predict outcomes of an entire time…
Simultaneous inference for time-varying models
Sayar Karmakar, Stefan Richter, Wei Biao Wu
A general class of time-varying regression models is considered in this paper. We estimate the regression coefficients by using local linear M-estimation. For these estimators, wea…
Time-varying auto-regressive models for count time-series
Arkaprava Roy, Sayar Karmakar
Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from COVID…
Bayesian modelling of time-varying conditional heteroscedasticity
Sayar Karmakar, Arkaprava Roy
Conditional heteroscedastic (CH) models are routinely used to analyze financial datasets. The classical models such as ARCH-GARCH with time-invariant coefficients are often inadequ…
Evaluating the Impact of COVID-19 on Cyberbullying through Bayesian Trend Analysis
Sayar Karmakar, Sanchari Das
COVID-19's impact has surpassed from personal and global health to our social life. In terms of digital presence, it is speculated that during pandemic, there has been a significan…
Change-Point Analysis of Cyberbullying-Related Twitter Discussions During COVID-19
Sanchari Das, Andrew Kim, Sayar Karmakar
Due to the outbreak of COVID-19, users are increasingly turning to online services. An increase in social media usage has also been observed, leading to the suspicion that this has…
Provable Training of a ReLU Gate with an Iterative Non-Gradient Algorithm
Sayar Karmakar, Anirbit Mukherjee
In this work, we demonstrate provable guarantees on the training of a single ReLU gate in hitherto unexplored regimes. We give a simple iterative stochastic algorithm that can trai…
Depth-2 Neural Networks Under a Data-Poisoning Attack
Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou
In this work, we study the possibility of defending against data-poisoning attacks while training a shallow neural network in a regression setup. We focus on doing supervised learn…
Analyzing initial stage of COVID-19 transmission through Bayesian time-varying model
Arkaprava Roy, Sayar Karmakar
Recent outbreak of the novel coronavirus COVID-19 has affected all of our lives in one way or the other. While medical researchers are working hard to find a cure and doctors/nurse…
Long-term prediction intervals of economic time series
Marek Chudy, Sayar Karmakar, Wei Biao Wu
We construct long-term prediction intervals for time-aggregated future values of univariate economic time series. We propose computational adjustments of the existing methods to im…
Uniqueness of communities in regular stochastic block models
Sayar Karmakar, Moumanti Podder
This paper studies the regular stochastic block model comprising \emph{several} communities: each of the non-overlapping communities, for , possesses vertice…
Optimal Gaussian Approximation for Multiple Time Series
Sayar Karmakar, Wei Biao Wu
We obtain an optimal bound for a Gaussian approximation of a large class of vector-valued random processes. Our results provide a substantial generalization of earlier results that…