BRJMS

Boston Research Journal of Mathematics & Statistics

BRJMS publishes double-blind, peer-reviewed, high-quality research in Mathematics and Statistics, featuring advanced studies in pure and applied mathematics, statistical theory, data science, and interdisciplinary mathematical applications. Every article is published open access under a CC BY 4.0 license. You keep your copyright, and your work is free to read worldwide from the day it publishes, across multiple channels.

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Aim & About Journal

The Boston Research Journal of Mathematics & Statistics primarily focuses on statistics and mathematical research. The journal contains original and progressive research papers in these disciplines in order to provide knowledge. We aim to become a reference research publication. We publish and review original and authentic research papers as well as auxiliary materials such as case studies, technical reports, etc. by scholars and researchers all across the world. We welcome the submission of papers concerning any branch of science and mathematics and their application in education, industry, research, and other organizations.

Primary Subjects Under This Discipline

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Pure Mathematics

Exploration of fundamental mathematical concepts and structures, including algebra, analysis, geometry, topology, and number theory, for intrinsic understanding.

Probability Theory & Stochastic Processes

The study of randomness and uncertainty, encompassing the mathematical models for random events, variables, and dynamic systems evolving over time.

Discrete Mathematics & Combinatorics

Study of finite or countable mathematical structures such as graphs, networks, codes, and arrangements, with applications in computer science and operations research.

Applied Mathematics

Development and utilization of mathematical methods and models to solve problems in science, engineering, finance, industry, and other practical domains.

Statistical Theory & Methodology

Foundations and techniques for data collection, analysis, interpretation, and inference, including experimental design, hypothesis testing, and model estimation.

Computational Mathematics & Data Science

Application of computational techniques to mathematical problems and the extraction of knowledge from data, including numerical analysis, optimization, and statistical learning.