ISSN (0. 10. 3- 0. Vol. 3 - - August 2. AN OFFICIAL JOURNAL OF THEINSTITUTE OF MATHEMATICAL STATISTICS. Articles. The Burr XII power series distributions: A new compounding family.
Rodrigo B. Silva, Gauss M. Cordeiro. 56. 5- 5. Bivariate sinh- normal distribution and a related model. Debasis Kundu. 59. Bayesian analysis and diagnostic of overdispersion models for binomial data.
University of Birmingham. Postgraduate study. This Mathematical Finance programme, taught jointly by the School of Mathematics and the Department of Economics.
Carolina C. Diniz, Rubiane M. Pires. 60. 8- 6. 39. Asymptotic distribution of the estimated parameters of an $\operatorname. Tomazella, S. Nadarajah. Estimates of the PDF and the CDF of the exponentiated Weibull distribution. M. Alizadeh, S. Baloui Jamkhaneh, S. Nadarajah. 69. 5- 7.
Almost sure central limit theorem for exceedance point processes of stationary sequences. Zhongquan Tan. 71. The Burr XII power series distributions: A new compounding family. Rodrigo B. Silva, Gauss M. Cordeiro, Braz. 3 (2. Abstract. Generalizing lifetime distributions is always precious for applied statisticians.
ISSN (0103-0752), Vol. 3 -- August 2015 Brazilian Journal of Probability and Statistics AN OFFICIAL JOURNAL OF THE INSTITUTE OF MATHEMATICAL STATISTICS. We provide excellent essay writing service 24/7. Enjoy proficient essay writing and custom writing services provided by professional academic writers. Mean variance portfolio optimizer software, including efficient frontier, active management statistics and risk attribution, by Peter Hoadley. SmartFolio is a state-of-the-art asset management software for investment professionals and private investors. It contains advanced portfolio optimization and risk. Modern portfolio theory (MPT), or mean-variance analysis, is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized. The probability cone software application can be used to provide a visual indication of the probable ranges of future asset prices and to dynamically.
![Black Litterman Optimization Software Black Litterman Optimization Software](http://systematicinvestor.files.wordpress.com/2011/11/plot2-small3.png)
In this paper, we introduce a new family of distributions by compounding the Burr XII and power series distributions. The compounding procedure follows the key idea by Adamidis and Loukas (Statist. Chahkandi and Ganjali (Comput. Morais and Barreto- Souza (Comput. The proposed family includes as a basic exemplar the Burr XII distribution. We provide some mathematical properties including moments, quantile and generating functions, order statistics and their moments, Kullback–Leibler divergence and Shannon entropy. The estimation of the model parameters is performed by maximum likelihood and the inference under large sample.
Two special models of the new family are investigated in details. We illustrate the potential of the new family by means of two applications to real data. It provides better fits to these data than other important lifetime models available in the literature. A lifetime distribution with decreasing failure rate.
Statistics and Probability Letters. Barakat, H. Computing the moments of order statistics from nonidentical random variables. Statistical Methods and Applications. Barreto- Souza, W., Morais, A. The Weibull- geometric distribution.
Journal of Statistical Computation and Simulation. Blundell, R., Duncan, A. Semiparametric estimation and consumer demand. Journal of Applied Econometrics. Boehme, T. Positive linear operators generated by analytic functions. SIAM Journal on Applied Mathematics. Chahkandi, M. On some lifetime distributions with decreasing failure rate.
Computational Statistics and Data Analysis. Cooner, F., Banerjee, S., Carlin, B. Flexible cure rate modeling under latent activation schemes. Journal of the American Statistical Association. Cover, T. Elements of Information Theory. New York: Wiley. Gradshteyn, I.
Table of Integrals, Series and Products. San Diego: Academic Press. Jaynes, E. Information theory and statistical mechanics. Physical Reviews. Kapur, J. Maximum Entropy Models in Science and Engineering.
New York: Wiley. Kus, C. A new lifetime distribution. Computational Statistics and Data Analysis. Lu, W. A new compounding life distribution: The Weibull–Poisson distribution. Journal of Applied Statistics. Mahmoudi, E. Generalized exponential power series distributions.
Computational Statistics and Data Analysis. Morais, A. A Compound family of Weibull and power series distributions. Computational Statistics and Data Analysis. Murthy, D. Weibull Models.
Wiley Series in Probability and Statistics. Hoboken, NJ: Wiley. Ostrovska, S. Positive linear operators generated by analytic functions.
Proceedings of the Indian Academy of Sciences. Parana. F., Ortega, E. M., Cordeiro, G. The Kumaraswamy Burr XII distribution: Theory and practice.
Journal of Statistical Computation and Simulation. Parana. F., Ortega, E. M., Cordeiro, G. The beta Burr XII distribution with application to lifetime data. Computational Statistics and Data Analysis. Prudnikov, A. P., Brychkov, Y. Integrals and Series, Vol. Amsterdam: Gordon and Breach Science Publishers.
Prudnikov, A. P., Brychkov, Y. Integrals and Series, Vol. Direct Laplace Transforms.
New York: Gordon and Breach Science Publishers. Rodrigues, C., Cordeiro, G. The Weibull negative binomial distribution. Advances and Applications in Statistics. Shannon, C. A mathematical theory of communication.
Bell System Technical Journal. Shao, Q. Notes on maximum likelihood estimation for the three- parameter Burr XII distribution. Computational Statistics and Data Analysis. Shao, Q. X., Wong, H., Xia, J. Models for extreme using the extended three- parameter Burr XII system with application to flood frequency analysis. Hydrological Sciences Journal.
Shore, J. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross- entropy. IEEE Transactions on Information Theory. Silva, R. B., Bourguignon, M., Dias, C. The compound family of extended Weibull power series distributions. Computational Statistics and Data Analysis. Silva, G. O., Ortega, E. M., Cancho, V. Log- Burr XII regression models with censored data.
Computational Statistics and Data Analysis. Soliman, A. Estimation of parameters of life from progressively censored data using Burr XII model. IEEE Transaction on Reliability. Soofi, E. Principal information theoretic approaches.
Journal of the American Statistical Association. Tahmasbi, R. A two- parameter lifetime distribution with decreasing failure rate. Computational Statistics and Data Analysis. Wu, J. W., Chen, Y. Statistical Inference based on progressively censored samples with random removals from the Burr type XII distribution. Journal of Statistical Computation and Simulation. Zografos, K. On families of beta- and generalized gamma- generated distributions and associated inference.
Statistical Methodology. Bivariate sinh- normal distribution and a related model. Debasis Kundu, Braz. Abstract. Sinh- normal distribution is a symmetric distribution with three parameters.
In this paper, we introduce bivariate sinh- normal distribution, which has seven parameters. Due to presence of seven parameters it is a very flexible distribution. Different properties of this new distribution has been established.
The model can be obtained as a bivariate Gaussian copula also. Therefore, using the Gaussian copula property, several properties of this proposed distribution can be obtained. Maximum likelihood estimators cannot be obtained in closed forms. We propose to use two step estimators based on Copula, which can be obtained in a more convenient manner. One data analysis has been performed to see how the proposed model can be used in practice. Finally, we consider a bivariate model which can be obtained by transforming the sinh- normal distribution and it is a generalization of the bivariate Birnbaum–Saunders distribution.
Several properties of the bivariate Birnbaum–Saunders distribution can be obtained as special cases of the proposed generalized bivariate Birnbaum–Saunders distribution. A new family of life distributions. Journal of Applied Probability.
Diaz- Garcia, J. Some generalizations of Birnbaum–Saunders and sinh- normal distributions. International Mathematical Forum. Gupta, P. On the multivariate normal hazard. Journal of Multivariate Analysis. Johnson, R. Applied Multivariate Statistical Analysis, 3rd ed. New Jersey, Englewood Cliffs: Prentice Hall.
Joe, H. Multivariate Model and Dependence Concepts. London: Chapman and Hall. Joe, H. Asymptotic efficiency of the two- stage estimation method for copula- based models.
Journal of Multivariate Analysis. Kundu, D., Balakrishnan, N. Bivariate Birnbaum–Saunders distribution and its associated inference. Journal of Multivariate Analysis. Marshall, A. Some comments on the hazard gradient.
Stochastic Processes and Its Applications. Meyer, C. The bivariate normal copula. Communications in Statistics—Theory and Methods. Nelsen, R. An Introduction to Copulas.
New York: Springer. Owen, W. Another look at the Birnbaum–Saunders distribution. Available at http: //www. MMR2. 00. 4/Extended%2. Abstract/WOwnn. pdf.