American Journal of Mathematical and Management Sciences Impact Factor & Information

Publisher: Taylor & Francis

Current impact factor: 0.00

Impact Factor Rankings

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
ISSN 0196-6324

Publisher details

Taylor & Francis

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Some individual journals may have policies prohibiting pre-print archiving
    • On author's personal website or departmental website immediately
    • On institutional repository or subject-based repository after either 12 months embargo
    • Publisher's version/PDF cannot be used
    • On a non-profit server
    • Published source must be acknowledged
    • Must link to publisher version
    • Set statements to accompany deposits (see policy)
    • The publisher will deposit in on behalf of authors to a designated institutional repository including PubMed Central, where a deposit agreement exists with the repository
    • STM: Science, Technology and Medicine
    • Publisher last contacted on 25/03/2014
    • This policy is an exception to the default policies of 'Taylor & Francis'
  • Classification

Publications in this journal

  • American Journal of Mathematical and Management Sciences 01/2016; 35(1):15-35. DOI:10.1080/01966324.2015.1065526

  • American Journal of Mathematical and Management Sciences 01/2016; 35(1):1-14. DOI:10.1080/01966324.2015.1050614
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this article, we consider the problem of testing the equality of two mean vectors when the data have a three-step monotone pattern of missing observations. We propose an approximate upper percentile of the Hotelling’s T 2-type statistic in which each dataset has a three-step monotone missing data pattern and the population covariance matrices are equal. Further, we obtain the Hotelling’s T 2-type statistics and their approximate upper percentiles in the case of data with unequal two-step monotone missing data patterns. We also consider multivariate multiple comparisons for mean vectors with three-step monotone missing data. Approximate simultaneous confidence intervals for pairwise comparisons among mean vectors and comparisons with a control are obtained using Bonferroni’s approximate upper percentiles of the T 2max · p and T 2max · c statistics, respectively. Finally, the accuracy of the approximations is investigated via Monte Carlo simulation.
    American Journal of Mathematical and Management Sciences 07/2015; 34(3). DOI:10.1080/01966324.2015.1020403
  • [Show abstract] [Hide abstract]
    ABSTRACT: In the present article, a confidence interval based on a combined estimator, a fusion of Bayes and frequentist estimators, is proposed. An evaluation has been done to compare it with the competing confidence intervals based on best frequentist estimator and Bayes estimator. The condition for the combined estimator to outperform the best frequentist estimator under true prior has been derived. The performance of the confidence interval based on the proposed combined estimator has been found to supersede the performance of the other competing estimators when comparison is made in terms of coverage probability or coverage probability per mean width. It has been observed that the superiority of one estimator over the other depends on the quality of the prior information. A Poisson experiment has been conducted to generate the data, on the basis of which a rigorous simulation study has been performed to reach a decision about the superiority of the proposed estimator.
    American Journal of Mathematical and Management Sciences 07/2015; 34(3). DOI:10.1080/01966324.2015.1036481
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this study, we have formulated an inventory model for deteriorating items with trade credit financing under profit maximization criteria, considering the demand as a linear function of time. Optimal solution procedures to find the optimal order quantity and cycle time are discussed. Several numerical examples are provided for the proposed model in order to illustrate the solution procedure. Sensitivity analysis of the optimal solution with respect to various parameters of the system is carried out, and the managerial implications are discussed in detail.
    American Journal of Mathematical and Management Sciences 04/2015; 34(3):197-212. DOI:10.1080/01966324.2014.1000551
  • [Show abstract] [Hide abstract]
    ABSTRACT: This article describes a multi-item inventory model, in which the available floor space or storage space is limited. The demand of items are assumed to be stock dependent and deterioration of items is assumed to be constant over time. The model is solved analytically in order to obtain the optimal solution of the problem. It is then illustrated with the help of numerical examples.
    American Journal of Mathematical and Management Sciences 04/2015; 34(2). DOI:10.1080/01966324.2014.980870
  • [Show abstract] [Hide abstract]
    ABSTRACT: Evaluation of biomarker performance in screening and diagnosis of a particular disease are topics of major interest in clinical diagnosis. The performance of diagnosis can be evaluated through the receiver operating characteristics (ROC) curve. In some situations, the variable/biomarker value for some of the subjects cannot be measured because of technical problems and we need to truncate the sample at some specific point. Discarding such observations will result in loss of valuable information. When the number of missing values in a sample is large, it will lead to biased estimates. Applying the traditional complete sample ROC procedures to the incomplete data to evaluate the accuracy may under- or overestimate the accuracy of classification. This article concerns modeling a parametric ROC curve for the left-truncated sample from Rayleigh distribution. The ROC model, area under the ROC curve (AUC) asymptotic variance and confidence interval for an estimated AUC have been discussed and analyzed through simulation studies as well as a real-life example.
    American Journal of Mathematical and Management Sciences 04/2015; 34(2). DOI:10.1080/01966324.2014.969461
  • [Show abstract] [Hide abstract]
    ABSTRACT: The concept of stochastic dominance has found important applications in the theory and applications of economics and statistics. It refers to a set of relations that may hold between a pair of distributions of random variables. In this article, we present a new definition of stochastic dominance using total time on test transform. The proposed definition based on total time on test is expressed in terms of quantile functions. The total time on test transform of order S is a generalization of the well-known Sth order stochastic dominance. A simple nonparametric test is developed to test the proposed stochastic dominance. It is shown that the distribution of the test statistic is asymptotically normal. Simulation studies are carried out to assess the efficiency of the test. We apply the proposed test to a real-life dataset of personal expenditures.
    American Journal of Mathematical and Management Sciences 02/2015; 34(2):162-183. DOI:10.1080/01966324.2014.971986

  • American Journal of Mathematical and Management Sciences 02/2015;
  • Article: hfhgfhgf

    American Journal of Mathematical and Management Sciences 02/2015;
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this article, we study and compare different proposals of heavy-tailed (possibly skewed) distributions as robust alternatives to the normal model. The density functions are all represented as scale mixtures, which enables efficient Bayesian estimation via Markov chain Monte Carlo (MCMC) methods. However, although the symmetric versions of these distributions are able to model heavy tails, they of course fail to capture asymmetry; for example, when the dataset contains extreme values in one of the tails. Therefore, distributions that accommodate skewness, as well as fat tails, are taken into account.
    American Journal of Mathematical and Management Sciences 01/2015; 34(1). DOI:10.1080/01966324.2014.969460