Estimation in the Birnbaum-Saunders distribution based on scale-mixture of normals and the EM-algorithm
Scale mixtures of normal (SMN) distributions are used for modeling symmetric data. Members of this family have appealing properties such as robust estimates, easy number generation, and efficient computation of the ML estimates via the EM-algorithm. The Birnbaum-Saunders (BS) distribution is a posit...
| Authors: | , , , |
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| Format: | article |
| Publication Date: | 2009 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2099/8949 |
| Online Access: | https://hdl.handle.net/2099/8949 |
| Access Level: | Open access |
| Keyword: | Numerical analysis--Simulation methods Stochastic differential equations--Numerical solutions Distribution (Probability theory) Birnbaum-Saunders distribution EM-algorithm Kurtosis Maximum likelihood methods Robust estimation Scale mixtures of normal distributions Anàlisi numèrica Distribució (Teoria de la probabilitat) Classificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations Classificació AMS::60 Probability theory and stochastic processes::60E Distribution theory Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Summary: | Scale mixtures of normal (SMN) distributions are used for modeling symmetric data. Members of this family have appealing properties such as robust estimates, easy number generation, and efficient computation of the ML estimates via the EM-algorithm. The Birnbaum-Saunders (BS) distribution is a positively skewed model that is related to the normal distribution and has received considerable attention. We introduce a type of BS distributions based on SMN models, produce a lifetime analysis, develop the EM-algorithm for ML estimation of parameters, and illustrate the obtained results with real data showing the robustness of the estimation procedure. |
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