Mohammad Ahsanullah

Rider University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Mohammad Shakil

Comilla Medical College

V. B. Nevzorov

St Petersburg University

B. M. Golam Kibria

Florida International University

Gane Samb Lô

Université Gaston Berger

G. G. Hamedani

Marquette University

Bander Al-Zahrani

King Abdulaziz University

Muhammad Qaiser Shahbaz

University of Engineering and Technology Lahore

Jacek Wesołowski

Korporacja Wschód (Poland)

George P. Yanev

Institute of Mathematics and Informatics

Essam K. AL-Hussaini

Kuwait University

Cooperative Institutions

University of Twente

Rider University

King Abdulaziz University

Benha University

Management Sciences (United States)

Marquette University

Quaid-i-Azam University

Sorbonne Université

McMaster University

Université Gaston Berger

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Record Range of Uniform Distribution

Journal of the Iranian Statistical Society (2005)

Mohammad Ahsanullah

We consider a sequence of independent and identicaly distributed (iid) random variables with absolutely continuous distri- bution function F(x) and probability density function (pdf) f(x). Let Rnl be the largest observation after observing nth record and R(ns) be the smallest observation after observing the nth record. Then we say Wnr = Rnl R(ns), n > 1, as the nth record range. We will consider some distributional properties of Wnr when f(x) = 1 ,0 x 1.

Sequence (biology)

Source

Cite

Citations (1)

Simple characterizations of Student's t2-distribution

Journal of the Royal Statistical Society Series D (The Statistician) (2003)

V. B. Nevzorov N. Balakrishnan Mohammad Ahsanullah

10.1111/1467-9884.00367

Cite

Citations (17)

A MULTIPLE REGRESSION MODEL FOR IDENTIFYING SOME RISK FACTORS AFFECTING THE CARDIOVASCULAR HEALTH ISSUES IN ADULTS

jnanabha (2023)

Mohammad Shakil Mohammad Ahsanullah B. M. Golam Kibria J. N. Singh Rakhshinda Jabeen

Multiple Regression analysis is one of the most critical and widely used statistical techniques in medical and applied research. It is dened as a multivariate technique for determining the correlation between a response variable and some combination of two or more predictor variables. Moreover, it is well-known in medical sciences that the obesity, high blood pressure and high cholesterol are major risk factors for cardiovascular health issues. The body mass index is a measure of body size, and combines a person's weight with their height, and therefore can affect their obesity, high blood pressure, high cholesterol and type 2 diabetes mellitus significantly, which are major risk factors for cardiovascular health issues in adults. Motivated by these facts, in this paper, a multiple linear regression model is developed to analyze the obesity in adults, based on a sample data of adult's age, height, weight, waist, diastolic blood pressure, systolic blood pressure, pulse, cholesterol, and the body mass index measurements. The use of multiple linear regression is illustrated in the prediction study of adult's obesity based on their body mass index. It is observed that in the presence of adult's age, weight, waist, diastolic blood pressure, systolic blood pressure, pulse, and cholesterol levels, height is a good predictor of the body mass index. Moreover, in the presence of age, height, waist, diastolic blood pressure, systolic blood pressure, pulse, and cholesterol levels, weight is a good predictor of the body mass index. Some concluding remarks are given in the end.

Pulse pressure

10.58250/jnanabha.2023.53214

Cite

Citations (0)

On a Generalized Burr Life-Testing Model: Characterization, Reliability, Simulation, and Akaike Information Criterion

Journal of Statistical Theory and Applications (2019)

Mohammad Ahsanullah Mohammad Shakil B. M. Golam Kibria Mohammed Elgarhy

introduced a generalized Burr increasing, decreasing, and upside-down bathtub failure rate life-testing model.In this paper, we provide some characterizations of this life-testing model by truncated first moment, order statistics and upper record values.We also investigate the reliability, simulation, and Akaike information criterion for this model.

Akaike information criterion

Bayesian information criterion

Characterization

10.2991/jsta.d.190818.001

Cite

Citations (0)

Asymptotic Behavior of the Middle and Intermediate Order Statistics

Atlantis studies in probability and statistics (2013)

Mohammad Ahsanullah V. B. Nevzorov Mohammad Shakil

We begin to study different limit theorems for order statistics. In this chapter we consider the asymptotic distributions for the so-called middle and intermediate order statistics.

10.2991/978-94-91216-83-1_10

Cite

Citations (0)

Generalized secant hyperbolic order statistics and associated inferences

Statistics (2006)

Mohammad Z. Raqab Mohammad Ahsanullah

Abstract In this article, we consider the generalized secant hyperbolic (GSH) distribution with known shape parameter t. Exact expressions for single and product moments of order statistics are established. The expressions are represented in terms of Riemann zeta, polygamma and hypergeometric functions. These special functions allow us to use a series of Mathematica procedures that will compute the means, variances and covariances of order statistics from the GSH distribution. The so-obtained values are used to compute the coefficients of the best linear unbiased estimators of the location and scale parameters. The variances of these estimators are also presented. A real data set has been performed to illustrate our findings. Keywords: Generalized secant hyperbolic distributionOrder statisticsPolygammaRiemann zetaHypergeometric functionsBest linear unbiased estimators Acknowledgements The authors are grateful to the referee for their comments and useful suggestions. The first author would like to thank the University of Jordan for supporting this research work.

10.1080/02331880600723127

Cite

Citations (0)

On Classification of Bivariate Distributions Based on Mutual Information

Journal of Statistical Research of Iran (2006)

Mohamed Habibullah Mohammad Ahsanullah

Among all measures of independence between random variables, mutual information is the only one that is based on information theory. Mutual information takes into account of all kinds of dependencies between variables, i.e., both the linear and non-linear dependencies. In this paper we have classified some well-known bivariate distributions into two classes of distributions based on their mutual information. The distributions within each class have the same mutual information. These distributions have been used extensively as survival distributions of two component systems in reliability theory.

Pointwise mutual information

Independence

Total correlation

Conditional mutual information

Interaction information

Information Theory

10.18869/acadpub.jsri.3.1.91

Cite

Citations (0)

On Generalized Type 1 Logistic Distribution

Afrika Statistika (2013)

Mohammad Ahsanullah

Some distributional properties of the generalized type 1 logistic distribution are given. Based on these distributional property a characterization of this distribution is presented.Key words: Conditional Expectation; Reversed Hazard Rate; Characterization.

Logistic distribution

Characterization

Conditional probability distribution

Generalized beta distribution

10.4314/afst.v8i1.1

Cite

Citations (1)

Asymptotic Behavior of the Extreme Order Statistics

Atlantis studies in probability and statistics (2013)

Mohammad Ahsanullah V. B. Nevzorov Mohammad Shakil

We continue to study the asymptotic properties of order statistics. Below the asymptotic behavior of extreme order statistics are under investigation.

10.2991/978-94-91216-83-1_11

Cite

Citations (0)

Estimation of the location and scale parameters of a power function distribution based on a selected number of order statistics

Communication in Statistics- Theory and Methods (1979)

Ahsanul Kabir Mohammad Ahsanullah

Suppose are the order statistics of a random sample of size n from a power-function distribution known. The best linear unbiased estimators of α and/or β based on k(≤ n) order statistics are obtained. It is found that the efficiencies are very high if (x(l), x(2)) or (x(l), x(n)) (depending on the value of γ) Is used for estimating α when β is known and (x(l), x(n)) is used for jointly estimating α and β. In estimating β when α is known, x(n) is proved to be the optimum order statistic.

Statistic

Power function

Location parameter

Value (mathematics)

Scale parameter

10.1080/03610927908827745

Cite

Citations (1)