Bhaswar B. Bhattacharya

• Kwonsang Lee, Bhaswar B. Bhattacharya, Jing Qin, Dylan Small (2023), A nonparametric binomial likelihood approach for causal inference in instrumental variable models, Journal of the Korean Statistical Society, 52 (), pp. 1055-1077.
• Sagnik Nandy and Bhaswar B. Bhattacharya (Under Review), Degree Heterogeneity in Higher-Order Networks: Inference in the Hypergraph β-Model.
• Bhaswar B. Bhattacharya, Anirban Chatterjee, Svante Janson (2023), Fluctuations of subgraph counts in graphon based random graphs, Combinatorics, Probability, and Computing, 32 (3), pp. 428-464. https://doi.org/10.1017/S0963548322000335
• Anirban Chatterjee and Bhaswar B. Bhattacharya (Under Review), Boosting the power of kernel two-sample tests.
• Sayak Chatterjee, Dibyendu Saha, Soham Dan, Bhaswar B. Bhattacharya (2023), Two-sample tests for inhomogeneous random graphs in $L_r$ norm: Optimality and asymptotics, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (PMLR), 206 (), pp. 6903-6911.
• Bhaswar B. Bhattacharya, Xiao Fang, Han Yan (2022), Normal approximation and fourth Moment theorems for monochromatic triangles, Random Structures and Algorithms, 60 (1), pp. 25-53.
• Somabha Mukherjee, Jaesung Son, Bhaswar B. Bhattacharya (2022), Estimation in Tensor Ising Models, A Journal of the IMA, A Journal of the IMA, 11 (4), pp. 1457-1500.
• Bhaswar B. Bhattacharya, Sayan Das, Sumit Mukherjee (2022), Motif estimation via subgraph sampling: the fourth moment phenomenon, Annals of Statistics, 50 (2), pp. 987-1011.
• Bhaswar B. Bhattacharya, Sayan Das, Somabha Mukherjee, Sumit Mukherjee (Under Review), Asymptotic distribution of random quadratic forms.
• Bhaswar B. Bhattacharya and Rajarshi Mukherjee (Under Review), Sparse uniformity testing.
• All Research from Bhaswar B. Bhattacharya »

Current Courses

• MATH8100 - Reading Seminar

  MATH8100034

• STAT4320 - Mathematical Statistics

  An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures. This course may be taken concurrently with the prerequisite with instructor permission.

  STAT4320001 ( Syllabus )

• STAT9700 - Mathematical Statistics

  Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and theory.

  STAT9700001 ( Syllabus )

Past Courses

• AMCS5999 - Independent Study

  Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• MATH4990 - Supervised Study

  Study under the direction of a faculty member. Intended for a limited number ofmathematics majors.

• MATH5999 - Independent Study

  Study under the direction of a faculty member. Hours to be arranged.

• STAT3990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll in this course.

• STAT4310 - Statistical Inference

  Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 1010 and 1020. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT4320 - Mathematical Statistics

  An introduction to the mathematical theory of statistics. Estimation, with a focus on properties of sufficient statistics and maximum likelihood estimators. Hypothesis testing, with a focus on likelihood ratio tests and the consequent development of "t" tests and hypothesis tests in regression and ANOVA. Nonparametric procedures. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT5110 - Statistical Inference

  Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course.

• STAT9700 - Mathematical Statistics

  Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and theory.

• STAT9910 - Sem in Adv Appl of Stat

  This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

• STAT9950 - Dissertation

  Dissertation

• STAT9990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll.

• NSF Career Award, 2021
• Alfred P. Sloan Research Fellowship, 2021
• Probability Dissertation Award, Department of Statistics, Stanford University, 2016
• Sabyasachi Roy Memorial Gold Medal for the best master's thesis, Statistics Program of the Indian Statistical Institute, 2011

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