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Publisher: CCSE   (Total: 41 journals)   [Sort by number of followers]

Showing 1 - 41 of 41 Journals sorted alphabetically
Applied Physics Research     Open Access   (Followers: 6)
Asian Culture and History     Open Access   (Followers: 15)
Asian Social Science     Open Access   (Followers: 8)
Cancer and Clinical Oncology     Open Access   (Followers: 7)
Computer and Information Science     Open Access   (Followers: 14)
Earth Science Research     Open Access   (Followers: 9)
Energy and Environment Research     Open Access   (Followers: 12)
Engineering Management Research     Open Access   (Followers: 7)
English Language and Literature Studies     Open Access   (Followers: 21)
English Language Teaching     Open Access   (Followers: 31)
Environment and Natural Resources Research     Open Access   (Followers: 7)
Environment and Pollution     Open Access   (Followers: 12)
Global J. of Health Science     Open Access   (Followers: 10, SJR: 0.416, CiteScore: 1)
Higher Education Studies     Open Access   (Followers: 60)
Intl. Business Research     Open Access   (Followers: 7)
Intl. Education Studies     Open Access   (Followers: 10)
Intl. J. of Biology     Open Access   (Followers: 2)
Intl. J. of Business and Management     Open Access   (Followers: 21)
Intl. J. of Chemistry     Open Access   (Followers: 12)
Intl. J. of Economics and Finance     Open Access   (Followers: 16)
Intl. J. of English Linguistics     Open Access   (Followers: 12)
Intl. J. of Marketing Studies     Open Access   (Followers: 20)
Intl. J. of Psychological Studies     Open Access   (Followers: 5)
Intl. J. of Statistics and Probability     Open Access   (Followers: 5)
J. of Agricultural Science     Open Access   (Followers: 13)
J. of Education and Learning     Open Access   (Followers: 3)
J. of Educational and Developmental Psychology     Open Access   (Followers: 17)
J. of Food Research     Open Access   (Followers: 3)
J. of Geography and Geology     Open Access   (Followers: 15)
J. of Management and Sustainability     Open Access   (Followers: 8)
J. of Materials Science Research     Open Access   (Followers: 8)
J. of Mathematics Research     Open Access   (Followers: 6)
J. of Molecular Biology Research     Open Access   (Followers: 3)
J. of Plant Studies     Open Access   (Followers: 1)
J. of Politics and Law     Open Access   (Followers: 11)
J. of Sustainable Development     Open Access   (Followers: 24)
Mechanical Engineering Research     Open Access   (Followers: 19)
Modern Applied Science     Open Access   (Followers: 2)
Network and Communication Technologies     Open Access   (Followers: 4)
Review of European Studies     Open Access   (Followers: 11)
Sustainable Agriculture Research     Open Access   (Followers: 3)
Similar Journals
Journal Cover
International Journal of Statistics and Probability
Number of Followers: 5  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1927-7032 - ISSN (Online) 1927-7040
Published by CCSE Homepage  [41 journals]
  • Reviewer Acknowledgements for International Journal of Statistics and
           Probability, Vol. 8, No. 5

    • Abstract: International Journal of Statistics and Probability wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal is greatly appreciated.Many authors, regardless of whether International Journal of Statistics and Probability publishes their work, appreciate the helpful feedback provided by the reviewers.Reviewers for Volume 8, Number 5Abdullah A. Smadi, Yarmouk University, JordanCarla J. Thompson, University of West Florida, USAChin-Shang Li, School of Nursing, USAEncarnación Alvarez-Verdejo, University of Granada, SpainFelix Almendra-Arao, UPIITA del Instituto Politécnico Nacional , MéxicoGabriel A. Okyere, Kwame Nkrumah University of Science and Technology, GhanaGane Samb Lo, University Gaston Berger, SENEGALGennaro Punzo, University of Naples Parthenope, ItalyGerardo Febres, Universidad Simón Bolívar, VenezuelaIvair R. Silva, Federal University of Ouro Preto – UFOP, BrazilMingao Yuan, North Dakota State University, USAPhilip Westgate, University of Kentucky, USAQingyang Zhang, University of Arkansas, USASajid Ali, Quaid-i-Azam University, PakistanSohair F. Higazi, University of Tanta, EgyptSubhradev Sen, Alliance University, IndiaVyacheslav Abramov, Swinburne University of Technology, AustraliaWei Zhang, The George Washington University, USAYuvraj Sunecher, University of Technology Mauritius, MauritiusZaixing Li, China University of Mining and Technology (Beijing), China Wendy SmithOn behalf of,The Editorial Board of International Journal of Statistics and ProbabilityCanadian Center of Science and Education
      PubDate: Fri, 30 Aug 2019 05:42:08 +000
  • On Generalized Gamma Distribution and Its Application to Survival Data

    • Abstract: The generalized gamma distribution is a continuous probability distribution with three parameters. It is a generalization of the two-parameter gamma distribution. Since many distributions commonly used for parametric models in survival analysis (such as the Exponential distribution , the Weibull distribution and the Gamma distribution) are special cases of the generalized gamma, it is sometimes used to determine which parametric model is appropriate for a given set of data. Generalized gamma distribution is one of the distributions used in frailty modeling. In this study , it is shown that generalized gamma distribution has three sub-families and its application to the analysis of a survival data has also been explored. The parametric modeling approach has been carried out to find the expected results.
      PubDate: Fri, 30 Aug 2019 05:41:29 +000
  • Bayesian Analysis of Sparse Counts Obtained From the Unrelated Question

    • Abstract: In sample surveys with sensitive items, sampled units may not respond or they respond untruthfully. Usually a negative answer is given when it is actually positive, thereby leading to an estimate of the population proportion of positives (sensitive proportion) that is too small. In our study, we have binary data obtained from the unrelated-question design, and both the sensitive proportion and the nonsensitive proportion are of interest. A respondent answers the sensitive item with a known probability, and to avoid non-identifiable parameters, at least two (not necessarily exactly two) different random mechanisms are used, but only one for each cluster of respondents. The key point here is that the counts are sparse (very small sample sizes), and we show how to overcome some of the problems associated with the unrelated question design. A standard approach to this problem is to use the expectation-maximization (EM) algorithm. However, because we consider only small sample sizes (sparse counts), the EM algorithm may not converge and asymptotic theory, which can permit normality assumptions for inference, is not appropriate; so we develop a Bayesian method. To compare the EM algorithm and the Bayesian method, we have presented an example with sparse data on college cheating and a simulation study to illustrate the properties of our procedure. Finally, we discuss two extensions to accommodate finite population sampling and optional responses.
      PubDate: Fri, 30 Aug 2019 02:06:54 +000
  • Lack-of-fit Testing for Polynomial Regression Models Without Replications

    • Abstract: Parametric and non-parametric approaches are developed to test the adequacy of the polynomial model Y=β°+j=1pβjXj+ε  when there is no replication in the values of the independent variable. The proposed tests avoid partitioning of the sample space of the continuous covariate. This paper suggests three tests based on the following concept: if the model is appropriate for a selected application, then the error component ε1,ε2,…,εn is a random sample with zero mean and constant variance. Simulation results are provided to illustrate the power and size of the proposed tests. An example is used to illustrate the methodologies. These tests are also compared with the classical lack-of-fit test to demonstrate their advantage.
      PubDate: Fri, 30 Aug 2019 01:56:57 +000
  • Visualizing State Identification in Auto-Regressive Hidden Markov (ARHMM)
           Models With the Forward and Backward Algorithms Using Excel

    • Abstract: Earlier articles, Laverty, Miket, Kelly (2002c), Laverty and Kelly (2019) used Excel to simulate Hidden Markov models and calculate the probabilities of the unknown states using the forward and backward algorithms (Rabiner, 1989). In those articles, independence between observations in each state were assumed. In many situations, however, the assumption of independence within states cannot be made. A more appropriate model for the data in this case would be an Autoregressive Hidden Markov model which accounts for serial correlation within states. In this article, a two-state ARHMM will be simulated with the forward-backward algorithm used to calculate conditional state probabilities given the observed data.
      PubDate: Fri, 30 Aug 2019 01:51:47 +000
  • Assessing Guaranteed Minimum Income Benefits and Rationality of Exercising
           Reset Options in Variable Annuities

    • Abstract: A variable annuity is an equity-linked financial product typically offered by insurance companies. The policyholder makes an upfront payment to the insurance company and, in return, the insurer is required to make a series of payments starting at an agreed upon date. For a higher premium, many insurance companies offer additional guarantees or options which protect policyholders from various market risks. This research is centered around two of these options: the guaranteed minimum income benefit (GMIB) and the reset option. The sensitivity of various parameters on the value of the GMIB is explored, particularly the guaranteed payment rate set by the insurer. Additionally, a critical value for future interest rates is calculated to determine the rationality of exercising the reset option. This will be able to provide insight to both the policyholder and policy writer on how their future projections on the performance of the stock market and interest rates should guide their respective actions of exercising and pricing variable annuity options. This can help provide details into the value of adding options to a variable annuity for companies that are looking to make variable annuity policies more attractive in a competitive market.
      PubDate: Fri, 30 Aug 2019 01:47:00 +000
  • On the Contextual Conditions Driving a Difficulty Factor

    • Abstract: This paper reports three simulation studies conducted to identify the contextual conditions leading to the observation of a difficulty factor in confirmatory factor analysis. The data of each study were generated to show one underlying source of responding only whereas the difficulties of the simulated items constituting the contextual condition were varied. The first study showed that a broad range of difficulties of items was insufficient for driving a difficulty factor. The second study revealed that very large and small difficulties of the same size could lead to a difficulty factor if the confirmatory factor model included two correlated factors. In the third study a subgroup of simulated items showed very large difficulties of the same size while the difficulties of the other simulated item were varied. In this study almost all combinations of difficulties led to the observation of a difficulty factor that was correlated or uncorrelated with the genuine factor.
      PubDate: Fri, 30 Aug 2019 01:43:38 +000
  • Exponentiated Nadarajah Haghighi Poisson Distribution

    • Abstract: This paper discusses the Exponentiated Nadarajah-Haghighi Poisson distribution focusing on statistical properties such as the Quantile, Moments, Moment Generating Functions, Order statistics and Entropy. To estimate the parameters of the model, the Maximum Likelihood Estimation method is used. To demonstrate the performance of the estimators, a simulation study is carried out. A real data set from Air conditioning system is used to highlight the potential application of the distribution.
      PubDate: Thu, 22 Aug 2019 10:03:19 +000
  • A Bivariate Index for Visually Measuring Marginal Inhomogeneity in Square

    • Abstract: For square tables, the marginal homogeneity model which has a structure that the row marginal distribution is equal to the column marginal distribution was proposed. Thereafter, various extended models of marginal homogeneity have been proposed, these models can be classified into two types marginal inhomogeneity. On the other hand, various indexes which measure the degree of deviation from marginal homogeneity have been proposed. However these indexes cannot concurrently define degrees of deviation from marginal homogeneity with respect to two types marginal inhomogeneity. This paper proposes a bivariate index that can concurrently define degrees of deviation from those. The proposed bivariate index would also be utility for visually comparing degrees of deviation from marginal homogeneity in several tables using confidence regions.
      PubDate: Thu, 22 Aug 2019 10:02:16 +000
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