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2 edition of Maximum likelihood estimation for loglinear models for the Birnbaum-Saunders distribution found in the catalog.

Maximum likelihood estimation for loglinear models for the Birnbaum-Saunders distribution

Maximum likelihood estimation for loglinear models for the Birnbaum-Saunders distribution

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Published by State University of New York, New York State Dept. of Health, in affiliation with Albany Medical College in [Albany, N.Y.?] .
Written in English

    Subjects:
  • Log-linear models.,
  • Distribution (Probability theory)

  • Edition Notes

    StatementJames Rieck and Jerry Nedelman.
    SeriesTechnical report series / University at Albany, School of Public Health, Department of Biometry and Statistics -- 1990, #1, Technical report series (State University of New York at Albany. Dept. of Biometry and Statistics) -- 1990, #1.
    ContributionsNedelman, Jerry., State University of New York at Albany. Dept. of Biometry and Statistics
    The Physical Object
    FormatMicroform
    Pagination29 p.
    Number of Pages29
    ID Numbers
    Open LibraryOL22258940M

      Birnbaum-Saunders Model Based on Skew-Normal Distribution: bst: Gradient Boosting: Maximum Likelihood Estimation of a Log-Concave Density Function: CNOGpro: Copy Numbers of Genes in prokaryotes: Approximate conditional inference for logistic and loglinear models: condformat: Conditional Formatting in Data Frames.


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Maximum likelihood estimation for loglinear models for the Birnbaum-Saunders distribution Download PDF EPUB FB2

Estimation of parameters in BS spatial loglinear models based on the maximum likelihood (ML) method needs a multivariate logarithmic version of the BS (log-BS) distribution; see Marchant et al.

The Birnbaum–Saunders (BS) distribution was derived to model failure times of materials subjected to fluctuating stresses and strains. Motivated by applications in the characterizations of materials, in Rieck and Nedelman proposed a log-linear model for the BS : Elizabeth González Patiño.

Some distributions have been proposed to extend the fatigue lifetime BS model. For example, Cordeiro et al. [2] defined the McDonald-Birnbaum-Saunders distribution, Ortega et al.

[3] introduced. For Maximum likelihood estimation for loglinear models for the Birnbaum-Saunders distribution book choices of sample sizes, we generated complete samples from the Birnbaum–Saunders distribution.

We report the coverage probabilities and the average interval lengths of the generalized confidence intervals at and confidence levels for each of α, μ, x and R () in Table 1, Table 2, Table 3, Table average interval lengths are given in by: Downloadable (with restrictions).

The β-Birnbaum–Saunders (Cordeiro and Maximum likelihood estimation for loglinear models for the Birnbaum-Saunders distribution book, ) and Birnbaum–Saunders (Birnbaum and Saunders, a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data.

We define the log-β-Birnbaum–Saunders distribution by the logarithm of the β-Birnbaum–Saunders distribution. An improved interval estimation for the two-parameter Birnbaum–Saunders distribution is discussed.

The proposed method is based on the recently developed higher-order likelihood-based asymptotic procedure. The probability coverages of confidence intervals are based on the proposed method and those procedures discussed Maximum likelihood estimation for loglinear models for the Birnbaum-Saunders distribution book Ng et al.

(Comput Cited by:   We study the problem of robust estimation for the two-parameter Birnbaum-Saunders distribution.

It is well known that the maximum likelihood estimator (MLE) is efficient when the underlying model is true but at the same time it is quite sensitive to data contamination that is often encountered in practice.

In this paper, we propose several estimators which have simple closed forms and are also Cited by: 5. Birnbaum, Z. and Saunders, S. Estimation for a family of life distributions with applications to fatigue. Journal of Applied Probability, 6, – Engelhardt, M. and Bain, L. and Wright, F.

Inferences on the parameters of the Birnbaum-Saunders fatigue life distribution based on maximum likelihood estimation.

distribution, and a comparison of the performance of different BS generators can be found in Rieck []. The maximum likelihood (ML) estimators of the shape and scale parameters based on a complete sample were discussed originally by Birnbaum and Saunders [44], and their asymp-totic distributions were obtained by Engelhardt et al.

[75].File Size: KB. The fl-Birnbaum{Saunders (Cordeiro and Lemonte, ) and Birnbaum{Saunders (Birnbaum and Saunders, a) distributions have been used quite efiectively to model failure times for ma-terials subject to fatigue and lifetime data. We deflne the log-fl-Birnbaum{Saunders distribution by the logarithm of the fl-Birnbaum{Saunders distribution.

Robust Parameter Estimation in the Weibull and the Birnbaum-Saunders Distribution Jing Zhao Clemson University, [email protected] Also, we consider the maximum likelihood estimation and graphical methods to compare the maximum likelihood estimation and Maximum likelihood estimation for loglinear models for the Birnbaum-Saunders distribution book method with the proposed method based on quantile.

We nd the advantages and. On Bivariate and Mixture of Bivariate Birnbaum-Saunders Distributions Mohsen Khosravi 1, Debasis Kundu2, Ahad Jamalizadeh Abstract Univariate Birnbaum-Saunders distribution has received a considerable amount of attention during the last few years.

Recently, Kundu et al. [9] introduced a bi-variate Birnbaum-Saunders Size: KB. A new generalized Birnbaum–Saunders distribution (Type-I GBS) was presented in Owen (IEEE Trans Reliab –, ) to model a lifetime of a product under cyclic stresses by using a long memory process on the crack extensions.

This highly flexible model includes the original BS distribution as a special case and can be widely applied in fatigue studies. In this article, we present Author: Ronghua Wang, Naijun Sha, Xiaoling Xu. One of the most studied topics in the BS distribution is its estimation and inference.

Several types of estimators for its original parameterization have been proposed. Birnbaum & Saunders (b) found its maximum likelihood (ML) estimators. Bhattacharyya & Fries () mentioned that the lack of an.

In this paper, we carry out robust modeling and influence diagnostics in Birnbaum‐Saunders (BS) regression models. Specifically, we present some aspects related to BS and log‐BS distributions and their generalizations from the Student‐ t distribution, and develop BS‐ t regression models, including maximum likelihood estimation based on.

distribution whose marginals are univariate Birnbaum–Saunders distributions. Estimation of the parameters by maximum likelihood is discussed and the Fisher’s information matrix is deter-mined.

A skewed bivariate version for the generalized Birnbaum–Saunders distribution is also introduced. bimodal extension of the Birnbaum-Saunders distribution (BS).

We show that maximum likelihood point estimation can be problematic since the standard nonlinear optimization algorithms may fail to converge. To deal with this prob-lem, we penalize the log-likelihood function. The numerical evidence we present shows that maximum likelihood Cited by: 2. Semi-Parametric Likelihood Inference for Birnbaum–Saunders Frailty Model where δi = Pm i j=1 δij, H0 is the cumulative baseline hazard function with parame- ter γ as given in (), f is the PDF of the BS distribution with shape parameter.

Generalized Linear Models for Insurance Data; Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance. Applied Stochastic Models in Business and Industry, Vol. 28, Issue. 1, p.

Maximum likelihood estimation of generalized linear models with Cited by: An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes.

VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The Birnbaum-Saunders distribution was derived in as a lifetime model for a specimen subjected to cyclic patterns of stresses and strains, and the ultimate failure of the specimen is assumed to be due to the growth of a dominant crack in the material.

The inverse Gaussian distribution is used to describe the first passage time for a particle (moving with constant velocity) that is subject Cited by: Birnbaum-Saunders Distribution. The Birnbaum-Saunders distribution was originally proposed as a lifetime model for materials subject to cyclic patterns of stress and strain, where the ultimate failure of the material comes from the growth of a prominent flaw.

The Birnbaum-Saunders distribution was originally proposed as a lifetime model for materials subject to cyclic patterns of stress and strain, where the ultimate failure of. Estimation in the Birnbaum–Saunders distribution based on scale-mixture of normals and the EM-algorithm.

Statistics and Operations Research Transacti – Balakrishnan, N., Gupta, R., Kundu, D. Leiva, V. and Sanhueza, A. On some mixture models based on the Birnbaum–Saunders distribution and associated inference. order to examine the performance of the proposed estimation method.

Moreover, to nd maximum likelihood estimates numerically, three methods of nding initial val-ues for the parameters - pseudo complete sample method, Type-II modi ed moment estimators of BS distribution method and stochastic approximation method - are : Yiliang Zhou.

The Birnbaum-Saunders (BS) distribution was introduced by Birnbaum & Saunders (b) to explain survival time and the stress produced in materials due to the cumulative damage laws for fatigue.

Tools for mixture models. MKLE: Maximum kernel likelihood estimation. mlbench: Machine Learning Benchmark Problems: mlCopulaSelection: Copula selection and fitting using maximum likelihood: MLDS: Maximum Likelihood Difference Scaling: MLEcens: Computation of the MLE for bivariate (interval) censored data: mlegp: Maximum Likelihood Estimates of.

Birnbaum-Saunders autoregressive conditional duration models applied to high-frequency financial data Helton Saulo1,2, Jeremias Leao˜ 3, V´ıctor Leiva 4,5∗, Robert d6 1Institute of Mathematics and Statistics, Universidade Federal de Goi´as, Brazil 2Department of Statistics, Universidade de Bras´ılia, Brazil 3Department of Statistics, Universidade Federal do Amazonas, Brazil.

Birnbaum–Saunders nonlinear regression models, which is useful in lifetime data analysis. Our results generalize those in Galea et al. [, Influence diagnostics in log-Birnbaum–Saunders regression models. Journal of Applied Statist –] which are confined to Birnbaum–Saunders linear re-gression models.

Journal of Data Science 15(), Modelling location, scale and shape parameters of the birnbaum-saunders generalized t distribution Luiz R. Nakamura1, Robert A. Rigby2, Dimitrios M. Stasinopoulos3, Roseli A. Leandro4, Cristian Villegas5, Rodrigo R.

Pescim6 1Departamento de Informatica e Estatstica, Universidade Federal de Santa Catarina 2STORM Research Centre, London File Size: KB. Nonparametric maximum likelihood estimation for random effect models: nppbib: Nonparametric Partially-Balanced Incomplete Block Design Analysis: npst: A generalization of the nonparametric seasonality tests of Hewitt et al.

() and Rogerson () nsRFA: Non-supervised Regional Frequency Analysis: numDeriv: Accurate Numerical Derivatives. Density, distribution function, quantile function and random generation for the Birnbaum-Saunders (fatigue life) distribution. R adalah sebuah aplikasi software gratis (berlisensi open-source GPL) dalam bentuk language dan environment untuk perhitungan dan pembuatan grafik yang berhubungan dengan statistik.

Software R merupakan projek GNU yang serupa dengan S language dan environment yang dikembangkan oleh John Chambers dan rekan-rekannya di Bell Laboratories (dahulunya AT&T. Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance., Applied Stochastic Models in Business and Industry, 28 16– doi/asmb Mathematical Reviews (MathSciNet): MR Digital Object Identifier: doi/asmbCited by: Publications.

Awarded papers. Leiva V, Barros M, Paula GA, Galea M () Influence diagnostics in log-Birnbaum-Saunders regression models with censored data.

Computational Statistics and Data Analysis 51(12) Article awarded by Elsevier to the most cited paper during two consecutive periods: / Harri Hietikko Comparison between the least squares, maximum likelihood and Bayes estimation of simple ARMA models Bernard Clement and Sukharanyan Chakraborty and Bimal K.

Sinha and Narayan C. Giri Tests for the mean. Asymptotic Theory for Maximum Likelihood Estimation Estimating Equations Statistical Quality Control And Reliability Analysis Using the Birnbaum-Saunders Distribution with Industrial Applications.- It next presents basic asymptotic approximations with one-dimensional parameter models as examples.

The book also describes. TZ oai:RePEc:eee:csdana:vyi:c:pRePEc:eee:csdana RePEc:eee:csdana:vyi:c:p Birnbaum-Saunders and Lognormal Kernel Estimators for Modelling Durations in High Frequency Financial Data Xiaodong Jin Department of Mathematics UNC at Charlotte, Charlotte, NC, USA E-mail: [email protected] and Janusz Kawczak Department of Mathematics UNC at Charlotte, Charlotte, NC, USA E-mail: [email protected]   Birnbaum-Saunders Model Based on Skew-Normal Distribution: bst: Maximum Likelihood Estimation of a Log-Concave Density Function: CNOGpro: Copy Numbers of Genes in prokaryotes: Approximate conditional inference for logistic and loglinear models: condGEE: Parameter estimation in conditional GEE for recurrent event gap times.

Computational Statistics & Data Analysis Pdf Number 2, Decem Aurelio R. Pdf. Oliveira and Mário A. Nascimento and Christiano Lyra Efficient implementation and benchmark of interior point methods for the polynomial $ L_1 $ fitting problem Aaron Childs and N.

Balakrishnan Some approximations to the multivariate hypergeometric distribution with applications to.bbmle Tools for general maximum likelihood estimation bs Package for the Birnbaum-Saunders distribution bsml Basis Selection from Multiple Libraries cond Approximate conditional inference for logistic and loglinear models condGEE Parameter estimation .demand data in inventory ebook see Leiva et al.

[ ] and Rojas et al. []. Our ebook objective is to explore the use of the BS distri-bution in inventory management. Di erently from previous studies that exclusively considered the e ects of one given distribution on inventory decision-making, we also analyze.