Anandamayee Majumdar

Anandamayee Majumdar
Inter-American Tropical Tuna Commission | IATTC

PhD

About

37
Publications
4,745
Reads
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435
Citations
Introduction
Anandamayee Majumdar currently works at Inter-American Tropical Tuna Commission. Anandamayee's research involves Bayesian Statistical Modeling, Spatio-temporal Processes, Econometrics. She is interested in multi-disciplinary applications.
Additional affiliations
November 2021 - present
Inter-American Tropical Tuna Commission
Position
  • Senior Statistician
Description
  • Dr. Ananda Majumdar joined the Stock Assessment Program in 2021. Her research focuses on a variety of topics including improving stock assessment methods and meeting estimation challenges when data sources are lacking, analyzing various kinds of univariate and multivariate fisheries data, developing sampling designs for diverse data collection programs including catch and effort, electronic monitoring, and mark-recapture.
January 2020 - September 2021
Independent University, Bangladesh
Position
  • Professor
Description
  • Teaching and Research
August 2015 - May 2019
North South University
Position
  • Professor
Description
  • Teaching and Research
Education
August 2001 - May 2004
University of Connecticut
Field of study
  • Bayesian Statistics, Spatio-temporal Statistics
August 1999 - May 2001
Michigan State University
Field of study
  • Statistics and Probability
August 1996 - May 1998
Indian Statistical Institute
Field of study
  • Mathematical Statistics and Probability

Publications

Publications (37)
Article
Full-text available
For point referenced spatial data, we often create explanatory mod-els which introduce regression structure with error consisting of a spa-tial term and a white noise term. Here, we consider more flexible regression structures which allow spatially varying regression coeffi-cients(Gelfand et al 2003). The resulting mean becomes a spatial response s...
Article
Full-text available
Ecologists increasingly use plot-scale data to inform research and policy related to regional and global environmental change. For soil chemistry research, scaling from the plot to the region is especially difficult due to high spatial variability at all scales. We used a hierarchical Bayesian model of plot-scale soil nutrient pools to predict stor...
Article
Full-text available
Soil pollution data collection typically studies multivariate measurements at sampling locations, e.g., lead, zinc, copper or cadmium levels. With increased collection of such multivariate geostatistical spatial data, there arises the need for flexible explanatory stochastic models. Here, we propose a general constructive approach for building suit...
Article
We use daily data for the period 25th November 1985 to 10th March 2020 to analyze the impact of newspapers-based measures of geopolitical risks (GPRs) on United States (US) Treasury securities by considering the level, slope and curvature factors derived from the term structure of interest rates of maturities covering 1 to 30 years. No evidence of...
Article
The IATTC port-sampling data are used to determine the species and size composition of the tropical tuna catch, and therefore play a very important role in the current Best Scientific Estimate (BSE) catch estimation methodology. The COVID-19 pandemic generally limited the ability of IATTC port-samplers to collect data in 2020 – 2021, however, the d...
Article
We develop a multivariate functional autoregressive model (MFAR), which captures the cross-correlation among multiple functional time series and thus improves forecast accuracy. We estimate the parameters under the Bayesian dynamic linear models (DLM) framework. In order to test for Granger causality from one FAR series to another we employ Bayes F...
Article
The increase in the estimated purse-seine catch of bigeye tuna (BET) in floating-object (OBJ) sets in 2020, relative to the previous year (BSE; e.g., Table A-7 in SAC-13-03), despite a decrease in the number of OBJ sets (SAC-13-06), and the marked disparity between the 2020 BSE and the reported catches from observers and logbooks, has raised concer...
Article
We study the temporal melodic (raga based as well as style based) and rhythmic (taala) patterns of Rabindranath Tagore’s annual song counts. Tagore used about fifteen main Hindustani classical ragas in his song compositions; he also developed new melodies using modulations and variations as well as blended styles, which give rise to a legion of mix...
Preprint
Full-text available
We develop a multivariate functional autoregressive model (MFAR), which captures the cross-correlation among multiple functional time series and thus improves forecast accuracy. We estimate the parameters under the Bayesian dynamic linear models (DLM) framework. In order to capture Granger causality from one FAR series to another we employ Bayes Fa...
Article
We analyse the out-of-sample forecasting ability of a time-varying metric of risk aversion for the entire term structure of US Treasury securities as reflected by the three latent factors, level, slope and curvature. Daily data cover the out-of-sample period 22nd June, 1988 to 3rd September, 2020 within a quantiles-based framework. The results show...
Article
Rabindranath Tagore (1861-1941) was a world famous Nobel laureate poet, philosopher, educationist and one of the most prolific song writers and composers in India’s history. He was the first Nobel laureate of Asian origin who received the Nobel prize in literature in 1913. Among his numerous manifestations of innovation, creation and excellence – h...
Article
We develop a time-varying measure of cay (cayTVP) using time-varying cointegration, and then compare the predictive ability of cayTVP with cay and a Markov-switching cay (cayMS) for excess stock returns and volatility in the US over the period 1952:Q2-2015:Q3, using a k-th order nonparametric causality-in-quantiles test. We find that time-varying c...
Article
Full-text available
Asymmetric spatial processes arise naturally in finance, economics, hydrology and ecology. For such processes, two different classes of models are considered in this paper. One of them, proposed by Majumdar and Paul (J Comput Graph Stat 25(3):727–747, 2016), is the Double Zero Expectile Normal (DZEXPN) process and the other is a version of the “ske...
Article
In this paper we test the forecasting ability of three estimated financial conditions indices (FCIs) with respect to key macroeconomic variables of output growth, inflation and interest rates. We do this by forecasting the aforementioned macroeconomic variables based on the information contained in the three alternative FCIs using a Bayesian VAR (B...
Article
Full-text available
The purpose of this paper is to investigate whether the current account balance can help in forecasting the quarterly S&P500-based equity premium out-of-sample. We consider an out-of-sample period of 1970:Q3 to 2014:Q4, with a corresponding in-sample period of 1947:Q2 to 1970:Q2. We employ a quantile predictive regression model. The quantile-based...
Article
Much significant research has been done to study how terror attacks affect financial markets. We contribute to this research by studying whether terror attacks, in addition to standard predictors considered in earlier research, help to predict gold returns. To this end, we use a Quantile-Predictive-Regression (QPR) approach that accounts for model...
Article
Full-text available
Human modification and management of urban landscapes drastically alters vegetation and soils, thereby altering carbon
Article
Information on economic policy uncertainty does matter in predicting the US equity premium, especially when accounting for structural instabilities and omitted nonlinearities in their relationship, via a quantile predictive regression approach over the monthly period 1900:1-2014:2. Unlike as suggested by a linear mean-based predictive model, the ex...
Article
Bivariate zero-inflated Poisson regression models have recently been used in various medical and biological settings to model excess zeros. However, there has not been any definite approach to deal with the same in the event of missing responses. The model itself is complex and as the responses are paired, missing values can occur in either or both...
Article
Full-text available
We introduce new classes of stationary spatial processes with asymmetric, sub-Gaussian marginal distributions using the idea of expectiles. We derive theoretical properties of the proposed processes. Moreover, we use the proposed spatial processes to formulate a spatial regression model for point-referenced data where the spatially correlated error...
Article
Full-text available
Given the existence of nonnormality and nonlinearity in the data generating process of real house price returns over the period of 1831-2013, this article compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period...
Article
This paper demonstrates a methodology for the automatic joint interpretation of high resolution airborne geophysical and space-borne remote sensing data to support geological mapping in a largely automated, fast and objective manner. At the request of the Geological Survey of Namibia (GSN), part of the Gordonia Subprovince of the Namaqua Metamorphi...
Article
Full-text available
Functional data arise in numerous areas nowadays. When the functional responses evolve with respect to time, the subjects may experience events at different paces with the conse- quence that the sample curves are improperly aligned for inferential purposes. In particular, the sample mean function without alignment will fail to produce a satisfactor...
Article
There is a need to construct valid covariance structures for modeling spatial data on the support of cylinder, for applications such as colon or esophagus cancer, heat mass transfer on cylindrical surfaces, sonar response on cylindrical surfaces or disease mapping on tree-trunks. Such processes could also be used to model spatio-temporal periodic p...
Conference Paper
Background/Question/Methods Research has shown that with human modification and management urban landscapes can have considerable carbon storage pools associated with both vegetation and soils, as well as experience increased rates of net primary productivity. However, as there are now a number of studies that indicate the social and ecological d...
Article
Forecasting aggregate retail sales may improve portfolio investors' ability to predict movements in the stock prices of retail chains. This paper uses 26 (23 single and 3 combination) forecasting models to forecast South Africa's aggregate seasonal retail sales. We use data from 1970:01–2012:05, with 1987:01–2012:05 as the out-of-sample period. Unl...
Article
Full-text available
This paper, first, estimates the appropriate, log-log or semi-log, linear long-run money demand relationship capturing the behavior US money demand over the period of 1980:Q1 to 2010:Q4, using the standard linear cointegration procedures found in the literature, and the corresponding nonparametric version of the same based on Projection Pursuit Reg...
Article
This paper uses small set of variables - real GDP, the inflation rate, and the short-term interest rate - and a rich set of models - athoeretical and theoretical, linear and nonlinear, as well as classical and Bayesian models - to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance by root mean...
Article
Full-text available
We analyze the multivariate spatial distribution of plant species diversity, distributed across three ecologically distinct land uses, the urban residential, urban non-residential, and desert. We model these data using a spatial generalized linear mixed model. Here plant species counts are assumed to be correlated within and among the spatial locat...
Article
Full-text available
We examine a flexible class of nonstationary stochastic models for multivariate spatial data proposed by Majumdar et al. (2010). This covariance model is based on convolutions of spatially varying covariance kernels with cen-ters corresponding to the centers of "local stationarity". A Bayesian method for estimation of the parameters in the model ba...
Article
This paper provides out-of-sample forecasts of Nevada gross gaming revenue and taxable sales using a battery of linear and non-linear forecasting models and univariate and multivariate techniques. The linear models include vector autoregressive and vector error-correction models with and without Bayesian priors. The non-linear models include non-pa...
Article
Full-text available
We propose a flexible class of nonstationary stochastic models for mul-tivariate spatial data. The method is based on convolutions of spatially varying covariance kernels and produces mathematically valid covariance structures. This method generalizes the convolution approach suggested by Majumdar and Gelfand (2007) to extend multivariate spatial c...
Article
Full-text available
Lately, bivariate zero-inflated (BZI) regression models have been used in many instances in the medical sciences to model excess zeros. Examples include the BZI Poisson (BZIP), BZI negative binomial (BZINB) models, etc. Such formulations vary in the basic modeling aspect and use the EM algorithm (Dempster, Laird and Rubin, 1977) for parameter estim...
Article
State lotteries employ sales projections to determine appropri- ate advertised jackpot levels for some of their games. This paper focuses on prediction of sales for the Lotto Texas game of the Texas Lottery. A novel prediction method is developed in this setting that utilizes functional data analysis concepts in conjunction with a Bayesian paradigm...
Article
Full-text available
Modeling the multivariate spatial distribution of soil carbon and nutrients has been a challenge for ecosystem ecologists. There is a need for explanatory models, which give insight into socio-economic and biophysical controls on soil spatial variability. We propose a hierarchical Bayesian modeling specification, an approach that takes into account...
Article
Full-text available
There is by now a substantial literature on spatio-temporal modeling. However, to date, there exists essentially no literature which addresses the issue of process change from a certain time. In fact, if we look at change points for purely time series data, the customary form is to propose a model involving a mean or level shift. We see little atte...
Article
This thesis addresses some problems in multivariate spatial and spatio-temporal modeling using a bayesian approach. The data are point referenced in a region. The thesis comprises three main parts. The first part discusses problems in spatio-temporal change-point modeling by introducing separable spatio-temporal covariance functions that change wit...

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Projects

Projects (2)
Project
We study the temporal melodic (raga based as well as style based) and rhythmic (taala) patterns of Rabindranath Tagore’s annual song counts. Tagore used about fifteen main Hindustani classical ragas in his song compositions; he also developed new melodies using modulations and variations as well as blended styles, which give rise to a legion of mixed melodies that are usually attributed to him; he also drew from the folk traditions and developed new versions of folk melodies (Sudhir Chanda, 2002). To be able to use ample data point for the study and inference, the melodic structure of most of his songs are studied here using four broad categorizations: (i) pure Hindustani raga based (ii) mixed or ‘Mishra’ melody based (or a blended version of two raga or two distinct styles) (iii) baul and (iv) kirtan. These categories together form necessary though not sufficient cohorts of his entire songs. We also check for changepoints in these different temporal patterns of melody and examine outliers. We then note the years of the outliers, to distinguish these years through his creative history. We also find associations of these distinct melodic styles among themselves, as well as with the corresponding poetic themes, to discover the categorical tendencies of song productions under each melodic style. We examine the temporal pattern of the most frequently used taala per year, as well the annual variation of taala in his songs. We examine possible changepoints in his use of taala variation. Keywords: baul, case study, changepoint, dependence, heteroscedasticity, kirtan, melody, outlier, Rabindranath Tagore, raga, song-counts, Tagore, taala, temporal pattern, time series
Project
I will focus on some of the best cross-over designs and using Bayesian modeling and inference estimate parameters that are difficult to estimate or find confidence intervals for using classical inference.