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Zhonghua Zhao - One of the best experts on this subject based on the ideXlab platform.

  • spatial structural characteristics of three hardwood species in korean pine broad leaved forest validating the Bivariate Distribution of structural parameters from the point of tree population
    Forest Ecology and Management, 2014
    Co-Authors: Gangying Hui, Zhonghua Zhao
    Abstract:

    Many indices can be used to describe the structural characteristics of tree populations at the forest stand scale. However, each of these indices can only express the whole or unilateral structure, i.e., none can simultaneously provide two or more aspects of the structural attributes of a tree population. The purpose of this study is to validate the Bivariate Distribution of structural parameters—a spatial structure analysis method based on the relationships between nearest-neighbor tree groups and to display its ability of analyzing tree population. Three common associated tree species, China ash (Fraxinus mandshurica Rupr.), Manchurian walnut (Juglans mandshurica Maxim.), and Amur lilac (Syringa amurensis Rupr.), in a mixed forest of China ash–Manchurian walnut were used as an example to validate the method. This type of mixed forest is one of the most common Korean pine broad-leaved forests in northeast China. Each population and resident individuals within different size classes were analyzed in detail. Our results demonstrate that the new method not only effectively reflects the similarities and differences among the three populations from two aspects, but also well represents the structural differentiation among trees within a species. The method provides much more structural information than most traditional methods, which may be helpful to further our understanding of population structure and to discover and protect biodiversity in the future.

  • the Bivariate Distribution characteristics of spatial structure in natural korean pine broad leaved forest
    Journal of Vegetation Science, 2012
    Co-Authors: Gangying Hui, Zhonghua Zhao
    Abstract:

    Aims Spatial structure is important in describing forest stand structure and change. We present a new method for the quantitative analysis of forest spatial structure based on the relationship of nearest neighbour tree groups. Location Six hundred m a.s.l., Dongdapo Natural Reserve, Jiaohe, Jilin Province, China Methods Six plots in three common stand types of natural Korean pine broad-leaved forest in northeast China were used to validate the method. Each plot measured 100 × 100 m, and all trees with DBH ≥5 cm were marked and located using a Total Station. We calculated Bivariate Distribution of the structural parameters, uniform angle index, mingling and dominance using Winkelmass and Excel software. Results Most trees in the forest were highly mixed by species and randomly distributed. Individuals with high DBH values were typically surrounded by other species; trees within stochastic Distribution patterns were usually surrounded by different species; and medium-sized trees were randomly distributed. Conclusions The Bivariate Distribution of structural parameters can provide more direct and useful information about the heterogeneity of spatial structure than can univariate Distributions or other conventional stand descriptors. This could be helpful for selective thinning in continuous cover forest management and in modelling and restoring forests.

Ergin Erdem - One of the best experts on this subject based on the ideXlab platform.

  • comparison of Bivariate Distribution construction approaches for analysing wind speed and direction data
    Wind Energy, 2011
    Co-Authors: Ergin Erdem
    Abstract:

    Statistical Distribution models for estimating wind energy potential spurred a great interest among researchers and practitioners recently. Bivariate statistical models for representing both wind direction and speed are helpful for the design and implementation of more efficient systems for harnessing wind energy. In this study, we construct seven different Bivariate joint Distributions based on three construction approaches, namely, angular-linear (AL), Farlie-Gumbel-Morgenstern (FGM) and anisotropic lognormal approaches, and then compare them using the adjusted R2 and root mean square error (RMSE) as measures of goodness of fit. For both AL and FGM approaches, the Distributions of wind speed and direction need to be obtained separately before the construction of joint Distribution. While using the mixture of von Mises Distribution for representing the wind direction, we utilize two different mixtures of Distributions for representing the wind speed, with one being the mixture of singly truncated below Normal and Weibull Distribution, and the other being the mixture of three-parameter inverse Gaussian and lognormal Distributions. A case study is conducted for this purpose on multiple sites in North Dakota, USA. It indicates that the two mixtures of Distributions for wind speed have comparable performances. Meanwhile, there is little difference in terms of adjusted R2 and RMSE values in modelling wind direction with AL and FGM approaches. Although the anisotropic approach significantly lags behind AL and FGM approaches, the adjusted R2 and RMSE values provided by the latter two approaches are comparable. Copyright © 2010 John Wiley & Sons, Ltd

Michael P Mcguire - One of the best experts on this subject based on the ideXlab platform.

  • Tornado-days in the United States by phase of the Madden–Julian oscillation and global wind oscillation
    Climate Dynamics, 2019
    Co-Authors: Todd W. Moore, Michael P Mcguire
    Abstract:

    Tornado activity in the United States varies over multiple time scales. The Madden–Julian oscillation (MJO) and global wind oscillation (GWO) are sources of climate variability at the seasonal and subseasonal scales. The univariate Distributions of tornado-days across the phases of these oscillations have been documented, but the Bivariate Distribution of tornado-days across both oscillations has not been. Here, the Bivariate Distribution of tornado-days across the phases of the MJO and GWO is documented and analyzed. Results herein refine the previously reported univariate Distributions and highlight variability across the phases of one of the oscillations while holding the phase of the other constant (e.g., tornado-days are more likely during phase 2 of the GWO, but the likelihood varies across the phase of MJO).

P. Yageen Thomas - One of the best experts on this subject based on the ideXlab platform.

  • On Weibull–Burr impounded Bivariate Distribution
    Japanese Journal of Statistics and Data Science, 2020
    Co-Authors: P. Yageen Thomas, Jitto Jose
    Abstract:

    In this work, we utilise a recently developed method of constructing a Bivariate Distributional model to generate a new Bivariate Distribution with Weibull and Burr type XII Distributions as its marginals. Some general characteristics of this newly generated Distribution together with some characterization results are further derived. The maximum likelihood method of estimation is applied for the estimation of the parameters of the constructed Distribution. The closeness of the maximum likelihood estimators with the true values of the parameters have been illustrated through a simulation study. We have identified the generated model as a suitable model for a real life Bivariate data set reported in the literature. We have also used another Bivariate data set on geoelectrical variables X and Y in which X represents the aquifer thickness and Y represents the coefficient of anisotropy to illustrate the results described in this paper for Bivariate Distributional modelling.

  • A New Bivariate Distribution with Rayleigh and Lindley Distributions as Marginals
    Journal of Statistical Theory and Practice, 2020
    Co-Authors: P. Yageen Thomas, Jitto Jose
    Abstract:

    Recently, a procedure of impounding a given marginal Distribution, with the auxiliary density function generated from the assumed form of the Distribution for the concomitant of an extreme order statistic of a sample, has been developed to create a Bivariate Distribution. In this paper we consider a marginal Rayleigh Distribution and impound it with the pdf of an extended form of Lindley Distribution to generate a new Bivariate Distribution. Some important properties of this new Bivariate model together with some characterization theorems have been also derived. Most commonly used reliability functions associated with the generated Bivariate Distribution as well have been obtained. The well-known maximum likelihood (ML) method of estimation is applied for the estimation of the parameters of the generated Bivariate Distribution. A simulation study is also carried out to illustrate the closeness of ML estimates with the true value of the parameters. A real-life Bivariate data set is used to demonstrate the application of the results of this paper in generating the appropriate Bivariate Distributional model in an innovative manner.

  • Role of concomitants of order statistics in determining parent Bivariate Distributions
    Communications in Statistics - Theory and Methods, 2016
    Co-Authors: T. G. Veena, P. Yageen Thomas
    Abstract:

    ABSTRACTIn this paper, we establish the role of concomitants of order statistics in the unique identification of the parent Bivariate Distribution. From the results developed, we have illustrated by examples the process of determination of the parent Bivariate Distribution using a marginal pdf and the pdf of either of the concomitant of largest or smallest order statistic on the other variable. An application of the results derived in modeling of a Bivariate Distribution for data sets drawn from a population as well is discussed.

  • On an Application of Concomitants of Order Statistics in Characterizing a Family of Bivariate Distributions
    Communications in Statistics - Theory and Methods, 2011
    Co-Authors: P. Yageen Thomas, T. G. Veena
    Abstract:

    In this article, we consider a family of Bivariate Distributions which includes the well-known Morgenstern family of Bivariate Distributions as its subclass. We identify some properties of concomitants of order statistics which characterize this generalized class of Distributions. An application of the characterization result in modeling a Bivariate Distribution to a data is also explained.

Debasis Kundu - One of the best experts on this subject based on the ideXlab platform.

  • On a new absolutely continuous Bivariate generalized exponential Distribution
    Statistical Methods & Applications, 2015
    Co-Authors: S. M. Mirhosseini, Debasis Kundu, M. Amini, A. Dolati
    Abstract:

    In this paper we studied a three-parameter absolutely continuous Bivariate Distribution whose marginals are generalized exponential Distributions. The proposed three-parameter Bivariate Distribution can be used quite effectively as an alternative to the Block and Basu Bivariate exponential Distribution. The joint probability density function, the joint cumulative Distribution function and its associated copula have simple forms. We derive different properties of this new Distribution. The maximum likelihood estimators of the unknown parameters can be obtained by solving simultaneously three non-linear equations. We propose to use EM algorithm to compute the maximum likelihood estimators, which can be implemented quite conveniently. One data set has been analyzed for illustrative purposes. Finally we propose some generalization of the proposed model.

  • Multivariate extension of modified Sarhan–Balakrishnan Bivariate Distribution
    Journal of Statistical Planning and Inference, 2011
    Co-Authors: Manuel Franco, Debasis Kundu, Juana-maría Vivo
    Abstract:

    Abstract Recently Kundu and Gupta [2010, Modified Sarhan–Balakrishnan singular Bivariate Distribution, Journal of Statistical Planning and Inference, 140, 526–538] introduced the modified Sarhan–Balakrishnan Bivariate Distribution and established its several properties. In this paper we provide a multivariate extension of the modified Sarhan–Balakrishnan Bivariate Distribution. It is a Distribution with a singular part. Different ageing and dependence properties of the proposed multivariate Distribution have been established. The moment generating function, the product moments can be obtained in terms of infinite series. The multivariate hazard rate has been obtained. We provide the EM algorithm to compute the maximum likelihood estimators and an illustrative example is performed to see the effectiveness of the proposed method.

  • modified sarhan balakrishnan singular Bivariate Distribution
    Journal of Statistical Planning and Inference, 2010
    Co-Authors: Debasis Kundu, Rameshwar D Gupta
    Abstract:

    Abstract Recently Sarhan and Balakrishnan [2007. A new class of Bivariate Distribution and its mixture. Journal of Multivariate Analysis 98, 1508–1527] introduced a new Bivariate Distribution using generalized exponential and exponential Distributions. They discussed several interesting properties of this new Distribution. Unfortunately, they did not discuss any estimation procedure of the unknown parameters. In this paper using the similar idea as of Sarhan and Balakrishnan [2007. A new class of Bivariate Distribution and its mixture. Journal of Multivariate Analysis 98, 1508–1527], we have proposed a singular Bivariate Distribution, which has an extra shape parameter. It is observed that the marginal Distributions of the proposed Bivariate Distribution are more flexible than the corresponding marginal Distributions of the Marshall–Olkin Bivariate exponential Distribution, Sarhan–Balakrishnan's Bivariate Distribution or the Bivariate generalized exponential Distribution. Different properties of this new Distribution have been discussed. We provide the maximum likelihood estimators of the unknown parameters using EM algorithm. We reported some simulation results and performed two data analysis for illustrative purposes. Finally we propose some generalizations of this Bivariate model.

  • Modified Sarhan–Balakrishnan singular Bivariate Distribution
    Journal of Statistical Planning and Inference, 2010
    Co-Authors: Debasis Kundu, Rameshwar D Gupta
    Abstract:

    Abstract Recently Sarhan and Balakrishnan [2007. A new class of Bivariate Distribution and its mixture. Journal of Multivariate Analysis 98, 1508–1527] introduced a new Bivariate Distribution using generalized exponential and exponential Distributions. They discussed several interesting properties of this new Distribution. Unfortunately, they did not discuss any estimation procedure of the unknown parameters. In this paper using the similar idea as of Sarhan and Balakrishnan [2007. A new class of Bivariate Distribution and its mixture. Journal of Multivariate Analysis 98, 1508–1527], we have proposed a singular Bivariate Distribution, which has an extra shape parameter. It is observed that the marginal Distributions of the proposed Bivariate Distribution are more flexible than the corresponding marginal Distributions of the Marshall–Olkin Bivariate exponential Distribution, Sarhan–Balakrishnan's Bivariate Distribution or the Bivariate generalized exponential Distribution. Different properties of this new Distribution have been discussed. We provide the maximum likelihood estimators of the unknown parameters using EM algorithm. We reported some simulation results and performed two data analysis for illustrative purposes. Finally we propose some generalizations of this Bivariate model.