The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

Ibrahim A Alwasel - One of the best experts on this subject based on the ideXlab platform.

  • Goodness‐of‐fit testing of a weak Memoryless Property of life distributions
    Australian & New Zealand Journal of Statistics, 2004
    Co-Authors: Ibrahim A Ahmad, Ibrahim A Alwasel
    Abstract:

    Summary A life distribution is said to have a weak Memoryless Property if its conditional probability of survival beyond a fixed time point is equal to its (unconditional) survival probability at that point. Goodness-of-fit testing of this notion is proposed in the current investigation, both when the fixed time point is known and when it is unknown but estimable from the data. The limiting behaviour of the proposed test statistic is obtained and the null variance is explicitly given. The empirical power of the test is evaluated for a commonly known alternative using Monte Carlo methods, showing that the test performs well. The case when the fixed time point t0 equals a quantile of the distribution F gives a distribution-free test procedure. The procedure works even if t0 is unknown but is estimable.

  • goodness of fit testing of a weak Memoryless Property of life distributions
    Australian & New Zealand Journal of Statistics, 2004
    Co-Authors: Ibrahim A Ahmad, Ibrahim A Alwasel
    Abstract:

    Summary A life distribution is said to have a weak Memoryless Property if its conditional probability of survival beyond a fixed time point is equal to its (unconditional) survival probability at that point. Goodness-of-fit testing of this notion is proposed in the current investigation, both when the fixed time point is known and when it is unknown but estimable from the data. The limiting behaviour of the proposed test statistic is obtained and the null variance is explicitly given. The empirical power of the test is evaluated for a commonly known alternative using Monte Carlo methods, showing that the test performs well. The case when the fixed time point t0 equals a quantile of the distribution F gives a distribution-free test procedure. The procedure works even if t0 is unknown but is estimable.

  • on goodness of fit testing of exponenttality using the Memoryless Property
    Journal of Nonparametric Statistics, 2001
    Co-Authors: Ibrahim A Alwasel
    Abstract:

    In this paper a test of fit for exponentiality based on the Memoryless Property is proposed. This procedure is applicable whether the scale and or the location parameters of the distribution are known or not The limiting behavior of the proposed test statistic is obtained and is shown to be normal under minimal conditions for both the null and null null distributions. The use of the proposed test is shown in an illustrative example. Also, empirical power of the test is evaluated for some commonly used alternatives using Monte Carlo methods. This shows that the test performs well compared to other procedures in the literature.

  • A Goodness‐of‐fit Test for Exponentiality Based on the Memoryless Property
    Journal of The Royal Statistical Society Series B-statistical Methodology, 1999
    Co-Authors: Ibrahim A Ahmad, Ibrahim A Alwasel
    Abstract:

    In this investigation a test of goodness of fit for exponentiality is proposed. This procedure applies equally whether the scale and/or the location parameters of the distribution are known or not. The limiting null and non-null distributions of the test statistic are normal under minimal conditions. Monte Carlo critical values for small sample sizes are given and the power of the test is calculated for various alternatives showing that it compares favourably relatively to other more complicated published procedures.

  • a goodness of fit test for exponentiality based on the Memoryless Property
    Journal of The Royal Statistical Society Series B-statistical Methodology, 1999
    Co-Authors: Ibrahim A Ahmad, Ibrahim A Alwasel
    Abstract:

    In this investigation a test of goodness of fit for exponentiality is proposed. This procedure applies equally whether the scale and/or the location parameters of the distribution are known or not. The limiting null and non-null distributions of the test statistic are normal under minimal conditions. Monte Carlo critical values for small sample sizes are given and the power of the test is calculated for various alternatives showing that it compares favourably relatively to other more complicated published procedures.

Masahito Hayashi - One of the best experts on this subject based on the ideXlab platform.

  • general nonasymptotic and asymptotic formulas in channel resolvability and identification capacity and their application to the wiretap channel
    IEEE Transactions on Information Theory, 2006
    Co-Authors: Masahito Hayashi
    Abstract:

    Several nonasymptotic formulas are established in channel resolvability and identification capacity, and they are applied to the wiretap channel. By using these formulas, the epsi capacities of the above three problems are considered in the most general setting, where no structural assumptions such as the stationary Memoryless Property are made on a channel. As a result, we solve an open problem proposed by Han and Verduacute. Moreover, we obtain lower bounds of the exponents of error probability and the wiretapper's information in the wiretap channel

  • General non-asymptotic and asymptotic formulas in channel resolvability and identification capacity and their application to wire-tap channel
    arXiv: Information Theory, 2005
    Co-Authors: Masahito Hayashi
    Abstract:

    Several non-asymptotic formulas are established in channel resolvability and identification capacity, and they are applied to wire-tap channel. By using these formulas, the $\epsilon$ capacities of the above three problems are considered in the most general setting, where no structural assumptions such as the stationary Memoryless Property are made on a channel. As a result, we solve an open problem proposed in Han & Verdu and Han. Moreover, we obtain lower bounds of the exponents of error probability and the wire-tapper's information in wire-tap channel.

  • General formulas for capacity of classical-quantum channels
    IEEE Transactions on Information Theory, 2003
    Co-Authors: Masahito Hayashi, Hiroshi Nagaoka
    Abstract:

    The capacity of a classical-quantum channel (or, in other words, the classical capacity of a quantum channel) is considered in the most general setting, where no structural assumptions such as the stationary Memoryless Property are made on a channel. A capacity formula as well as a characterization of the strong converse Property is given just in parallel with the corresponding classical results of Verdu-Han (1994) which are based on the so-called information-spectrum method. The general results are applied to the stationary Memoryless case with or without cost constraint on inputs, whereby a deep relation between the channel coding theory and the hypothesis testing for two quantum states is elucidated.

Ibrahim A Ahmad - One of the best experts on this subject based on the ideXlab platform.

  • goodness of fit testing of a weak Memoryless Property of life distributions
    Australian & New Zealand Journal of Statistics, 2004
    Co-Authors: Ibrahim A Ahmad, Ibrahim A Alwasel
    Abstract:

    Summary A life distribution is said to have a weak Memoryless Property if its conditional probability of survival beyond a fixed time point is equal to its (unconditional) survival probability at that point. Goodness-of-fit testing of this notion is proposed in the current investigation, both when the fixed time point is known and when it is unknown but estimable from the data. The limiting behaviour of the proposed test statistic is obtained and the null variance is explicitly given. The empirical power of the test is evaluated for a commonly known alternative using Monte Carlo methods, showing that the test performs well. The case when the fixed time point t0 equals a quantile of the distribution F gives a distribution-free test procedure. The procedure works even if t0 is unknown but is estimable.

  • Goodness‐of‐fit testing of a weak Memoryless Property of life distributions
    Australian & New Zealand Journal of Statistics, 2004
    Co-Authors: Ibrahim A Ahmad, Ibrahim A Alwasel
    Abstract:

    Summary A life distribution is said to have a weak Memoryless Property if its conditional probability of survival beyond a fixed time point is equal to its (unconditional) survival probability at that point. Goodness-of-fit testing of this notion is proposed in the current investigation, both when the fixed time point is known and when it is unknown but estimable from the data. The limiting behaviour of the proposed test statistic is obtained and the null variance is explicitly given. The empirical power of the test is evaluated for a commonly known alternative using Monte Carlo methods, showing that the test performs well. The case when the fixed time point t0 equals a quantile of the distribution F gives a distribution-free test procedure. The procedure works even if t0 is unknown but is estimable.

  • A Goodness‐of‐fit Test for Exponentiality Based on the Memoryless Property
    Journal of The Royal Statistical Society Series B-statistical Methodology, 1999
    Co-Authors: Ibrahim A Ahmad, Ibrahim A Alwasel
    Abstract:

    In this investigation a test of goodness of fit for exponentiality is proposed. This procedure applies equally whether the scale and/or the location parameters of the distribution are known or not. The limiting null and non-null distributions of the test statistic are normal under minimal conditions. Monte Carlo critical values for small sample sizes are given and the power of the test is calculated for various alternatives showing that it compares favourably relatively to other more complicated published procedures.

  • a goodness of fit test for exponentiality based on the Memoryless Property
    Journal of The Royal Statistical Society Series B-statistical Methodology, 1999
    Co-Authors: Ibrahim A Ahmad, Ibrahim A Alwasel
    Abstract:

    In this investigation a test of goodness of fit for exponentiality is proposed. This procedure applies equally whether the scale and/or the location parameters of the distribution are known or not. The limiting null and non-null distributions of the test statistic are normal under minimal conditions. Monte Carlo critical values for small sample sizes are given and the power of the test is calculated for various alternatives showing that it compares favourably relatively to other more complicated published procedures.

Cheol Oh - One of the best experts on this subject based on the ideXlab platform.

  • traffic flow forecasting based on pattern recognition to overcome Memoryless Property
    Multimedia and Ubiquitous Engineering, 2007
    Co-Authors: Cheol Oh
    Abstract:

    A variety of methods and techniques have been developed to forecast traffic flow. Current nearest neighbor non-parametric traffic flow forecasting models treat the dynamic evolution of traffic flows at a given state as a Memoryless process; the current state of traffic flow entirely determines the future state of traffic flow, with no dependence on the past sequences of traffic flow patterns that produced the current state. Since traffic flow is not completely random in nature, there should be some patterns in which the past traffic flow repeats itself. In this paper, we proposed a pattern recognition technique, which enables us to consider the past sequences of traffic flow patterns to predict the future state. It was found that the pattern recognition model is capable of predicting the future state of traffic flow reasonably well compared with the k-nearest neighbor non-parametric regression model.

  • MUE - Traffic Flow Forecasting Based on Pattern Recognition to Overcome Memoryless Property
    2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07), 2007
    Co-Authors: Cheol Oh
    Abstract:

    A variety of methods and techniques have been developed to forecast traffic flow. Current nearest neighbor non-parametric traffic flow forecasting models treat the dynamic evolution of traffic flows at a given state as a Memoryless process; the current state of traffic flow entirely determines the future state of traffic flow, with no dependence on the past sequences of traffic flow patterns that produced the current state. Since traffic flow is not completely random in nature, there should be some patterns in which the past traffic flow repeats itself. In this paper, we proposed a pattern recognition technique, which enables us to consider the past sequences of traffic flow patterns to predict the future state. It was found that the pattern recognition model is capable of predicting the future state of traffic flow reasonably well compared with the k-nearest neighbor non-parametric regression model.

Ratan Dasgupta - One of the best experts on this subject based on the ideXlab platform.

  • Tuber Crop Growth Model, Performance Rate, and Some Characterization Theorems
    Advances in Growth Curve and Structural Equation Modeling, 2018
    Co-Authors: Ratan Dasgupta
    Abstract:

    Geometric and exponential distributions may be used for modeling number of tubers and yield of crop. Geometric distribution is discrete version of appropriate exponential distribution and both the distributions have Memoryless Property. We model a real dataset on number of potato tubers arising from a growth experiment conducted in Giridih farmland and study the properties of these and related distributions in terms of performance rate (Dasgupta 2018) and hazard rate. Some characterization theorems are proved for discrete and continuous random variables.

  • Growth of tuber crops and almost sure band for quantiles
    Communications in Statistics - Simulation and Computation, 2015
    Co-Authors: Ratan Dasgupta
    Abstract:

    ABSTRACTSome tuber crops are governed by Memoryless Property of exponential distribution leading to a mixture distribution with heavy tail. Quantile-based estimators may then be appropriate than mean as a measure of central tendency. We prove almost sure representation theorems for sample quantiles in a general setup of U statistics, under slightly stronger assumption than assuming the existence of a continuously differentiable distribution function F for the kernel h. We obtain almost sure (a.s.) upper and lower estimate for F− 1(p), p ∈ (0, 1) as a band for p varying. As an application, dataset arising from two varieties of potato cultivation are analyzed.

  • Tuber Crop Growth and Pareto Model
    Springer Proceedings in Mathematics & Statistics, 2013
    Co-Authors: Ratan Dasgupta
    Abstract:

    Pareto distribution has wide applications in natural sciences. We show that yield of some crops may be modeled by a Pareto like density from growth viewpoint of additional tubers. Extending a result of Dasgupta (Discrete distributions with application to lifestyle data. In International conference on productivity, quality, reliability, optimization and modeling proceedings (Vol. 1, pp. 502–520), New Delhi: Allied Publishers, 2011), we obtain an analytic expression for density of the variable that is a mixture of exponential densities relevant in real life situations; a beta prior induced on exponential of intensity function for variables with Memoryless Property (viz., exponential random variables) results in a heavy tailed distribution much like a Pareto variable. This may be appropriate for modeling some real life situations when Memoryless Property of a variable may hold only in subgroups. The results are applied to model yield of tuber crops with real data sets.