American Journal of Mathematical and Management Sciences Impact Factor & Information

Publisher: Taylor & Francis

Journal description

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

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    • Author can archive a pre-print version
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    • 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
    ​ green

Publications in this journal

  • [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
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    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
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    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
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    ABSTRACT: A Maximum Likelihood Estimator (MLE) approach is considered for the estimation of a binomial proportion parameter in doubly sampled data subject to false positive misclassification. We assume that an inexpensive, error-prone device is used on a large main study and an expensive, error-free device is utilized on a smaller substudy. This double sample allows identifiability of all unknown parameters, because by incorporating additional information (data) via double sampling, the dimension of sufficient statistics is greater than or equal to the numbers of parameters; hence, the model becomes identifiable. Additionally, we derive two confidence intervals (CIs): a naïve Wald CI and a modified Wald CI, and we compare the performance of these two CIs in terms of coverage probability and average length, via a Monte Carlo simulation. We then apply the two newly derived estimator and confidence intervals to a real data problem.
    American Journal of Mathematical and Management Sciences 02/2015; 34(2):184-196. DOI:10.1080/01966324.2014.987411
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  • American Journal of Mathematical and Management Sciences 02/2015;
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    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
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    ABSTRACT: For any absolutely continuous random variable with cumulative distribution function G(x; ), Zografos and Balakrishnan (2009) pioneered the generalized Γ-G(x; a, ) family of distributions with an additional positive parameter a. We provide a comprehensive treatment of the mathematical properties of the Γ-G family when G is the logistic distribution. The Γ-logistic distribution is studied in detail by considering its moments, generating function, mean deviations, Rényi entropy, and order statistics. We use maximum likelihood for estimating the model parameters and provide the elements of the observed information matrix. A real data set is given to illustrate the use of the new model.
    American Journal of Mathematical and Management Sciences 01/2015; 34(1). DOI:10.1080/01966324.2014.954296
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    ABSTRACT: Continuous improvement rules such as Six Sigma enable companies to improve customer satisfaction and loyalty in an increasingly competitive environment to meet customers’ expectations. The success of Six Sigma execution can be computed by evaluating the effectiveness of the developed software projects. This includes keeping the software team focused and stimulating their interest. An excellent computation system should be able to calculate and compare software projects of several types and necessities. This computation system to measure effectiveness should be extensive, objective, and acceptable to all. A strong effectiveness measure is formulated in this research on the basis of a fuzzy approach, considering the following serious software success factors for Six Sigma implementation: involvement of software top management, staff application knowledge, staff tool and team skills, depth of software analysis and requirements to establish the root cause(s), timeline followed to complete the software project, and review and monitoring of the software project. The Six Sigma software projects producing the intended results of the evaluation system generally depend on the human perception that can result in unreliable computation. To overcome this fault or shortcoming, in this article we used a fuzzy logic expert system approach based on fuzzy linguistic parameters and fuzzy values for calculating the software project effectiveness by using software management perception to overcome unreliable computation due to human perception. The article defines suitable importance and performance grading for each selected factor for the effective implementation of Six Sigma software projects by using linguistic variables. The main advantage of this research is for the practicing organizations to utilize this system of methods for impartial evaluation of developed software projects.
    American Journal of Mathematical and Management Sciences 01/2015; 34(1). DOI:10.1080/01966324.2014.955222
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    ABSTRACT: In order to ensure that a system functions up to a mission time, it is a common practice to enhance the random system life by allocating redundant components to the constituent components of the system. There is more literature on active redundancy allocation than on cold-standby redundancy. The decision to use active redundancy or cold-standby redundancy depends on the required safety level, response time, and power consumption. Though active redundancy achieves a higher safety level, the cold standby consumes less power and, hence, is more economical. Adding redundancy does not ensure stochastic optimization of system life unless it is allocated properly. A new methodology for finding an optimal solution for a cold redundancy allocation problem has been developed in this study. This article discusses how to select a constituent component of a system to which the redundant component is to be added in order to maximize the system life stochastically, when the component lives are independently distributed random variables. The proposed method is capable of handling different types of complex system designs. Numerical examples are given to illustrate the method delineated here.
    American Journal of Mathematical and Management Sciences 01/2015; 34(1). DOI:10.1080/01966324.2014.954295
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    ABSTRACT: A generalized version of the three-parameter transmuted inverse Weibull distribution is introduced in this article. This distribution generalizes the following distributions: (a) transmuted inverse exponential, (b) transmuted inverse Rayleigh, (c) inverse Weibull. The properties of the transmuted inverse Weibull distribution are discussed. This model is capable of modeling various shapes of aging and failure criteria. Here we present the relationship between shape parameter, probability density function, cumulative distribution function, reliability function, hazard function, percentile life, mean, variance, coefficient of variation, coefficient of skewness, and coefficient of kurtosis. We derive the moments, geometric mean, harmonic mean, entropy, mean deviation, and examine the order statistics with their moments. We propose the method of maximum likelihood for estimating the model parameters and obtain the observed information matrix. The usefulness of the new distribution is shown by application to real data and comparison with other distributions.
    American Journal of Mathematical and Management Sciences 11/2014; 33(4):261-286. DOI:10.1080/01966324.2014.929989
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    ABSTRACT: Balakrishnan and Li (2005) introduced the use of ordered ranked set sampling (ORSS) and derived the best linear unbiased estimators (BLUEs) under ORSS (BLUEs-ORSS). In this study, we extend the work to ordered double ranked set sampling (ODRSS) scheme by using the idea of order statistics from independent and nonidentically distributed random variables. The BLUEs of the location and the scale parameters of a location-scale family of distributions are derived using ODRSS (BLUEs-ODRSS). It is shown that the BLUEs-ODRSS are uniformly better than the BLUEs-ORSS for the two-parameter exponential, normal, and generalized geometric distributions. Furthermore, we also study the properties of the distribution-free confidence intervals for quantiles and tolerance intervals based on ODRSS. We show that the confidence and tolerance intervals under the ODRSS scheme are more precise than their counterparts based on the ORSS scheme.
    American Journal of Mathematical and Management Sciences 10/2014; 33(4). DOI:10.1080/01966324.2014.929988
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    ABSTRACT: The additive reversed hazards model relates the conditional reversed hazard function of the lifetime linearly to the covariates. It describes the association between the lifetime and covariates in terms of risk difference. In the present work, we introduce an additive reversed hazards model for modeling and analysis of lifetime in the presence of covariates under left censoring. We develop a closed form semiparametric estimator of the regression parameter. We also provide a Breslow-type estimator for a cumulative baseline reversed hazard function. Asymptotic properties of the estimators are studied. Simulation studies are conducted to assess the finite sample properties of the estimators. Finally, we apply the proposed model to real-life data.
    American Journal of Mathematical and Management Sciences 10/2014; 33(4). DOI:10.1080/01966324.2014.943600
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    ABSTRACT: The aim of this article is to present a fuzzy programming approach to a single-sink, fixed-charge, multiobjective, multi-index stochastic transportation problem (SSMISTP). This article focuses on the minimization of the transportation cost, deterioration rate, and underused capacity for transportation of raw materials from different sources to the ”Single-Sink” by different transportation modes. The parameters of the proposed problem are transportation cost, fixed-charge, deterioration rate, and underused capacity. These parameters are to be treated here as random variables. Because of the globalization of the market, assume that the “Sink” demand is an interval representing the inexact demand component for the SSMISTP. By considering the uncertainties of these parameters, we formulate the mathematical model of the proposed problem. Using a stochastic programming approach, all the stochastic objective functions are converted into deterministic objective functions. Again using the interval concept, the proposed interval-valued sink constraint decomposes into two deterministic constraints. Finally, using all deterministic objective functions and constraints, we design a multiobjective transportation problem(MOTP). The optimal compromise solution to the MOTP has been obtained by using a fuzzy programming technique. We demonstrate the feasibility of the proposed problem using a real-life practical example.
    American Journal of Mathematical and Management Sciences 10/2014; 33(4). DOI:10.1080/01966324.2014.942474
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    ABSTRACT: In this article we propose a new scheduling model in which two agents share a common machine to process their respective jobs. Each agent has its own objective function to optimize, and the job processing times are subject to cumulative deteriorating. The task is to minimize one agent's total completion time with the restriction that another agent's maximum cost cannot exceed a given upper bound. We analyze some properties on the optimal solution and then propose an optimal polynomial-time solution algorithm followed by a numerical example.
    American Journal of Mathematical and Management Sciences 10/2014; 33(4). DOI:10.1080/01966324.2014.929990
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    ABSTRACT: In this article we study the problem of selecting among k independent Bernoulli populations whose success probabilities are unknown, and the sample size for each population is assumed to follow a discrete uniform distribution with known range. We consider two goals and propose procedures for each goal: (1) selecting the best and (2) selecting the best in comparison with a standard. The “best” is defined as that having the highest success probability. We derive the probability of a correct selection and the least favorable configuration for each procedure by using the exact binomial distribution, without any approximation. Simulations and examples are provided to illustrate our procedures.
    American Journal of Mathematical and Management Sciences 07/2014; 33(3). DOI:10.1080/01966324.2014.923352