Probabilidade de Cobertura dos Intervalos de Confiança ... - Uem

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76 77 proc p r i n t t o ; run ; 78 79 data n u l l ; 80 s e t emvs ( keep=parameter estimate s t a n d a r d e r r o r ) ; 81 nda = &i n d i c e ; 82 f i l e ” c : \ emv b &n&p . . dat ” mod ; 83 put nda ” ; ” parameter ” ; ” estimate ” ; ” s t a n d a r d e r r o r ; 84 run ; 85 86 data n u l l ; 87 s e t adic ( keep=l a b e l estimate s t a n d a r d e r r o r ) ; 88 nda = &i n d i c e ; 89 f i l e ” c : \ a d i c b &n&p . . dat ” mod ; 90 put nda ” ; ” l a b e l ” ; ” estimate ” ; ” s t a n d a r d e r r o r ; 91 run ; 92 %mend bootstrap ; 93 %macro geracao (mu=, beta =,n=, seed =,p=); 94 data o r i g i n a l ( drop=i U) ; 95 do i = 1 to &n ; 96 U = uniform(&seed ) ; 97 time = &mu∗(− l o g (1−U))∗∗(1/& beta ) ; 98 d e l t a = ranbin(&seed ,1 ,&p ) ; 99 output ; 100 end ; 101 %mend geracao ; 102 %macro cobertura (mu=, beta =,p=,n=,nboot1=,nboot2 =); 103 %d e l e t a (n=&n , p=&p ) ; 104 %do j = 1 %to &nboot1 ; 105 %geracao (mu=&mu, beta=&beta , n=&n , seed=&j ∗1000 ,p=&p ) ; 106 %estimacao dados ( dados=o r i g i n a l ,mu=&mu, beta=&beta , i n d i c e=&j , n=&n , p=&p ) ; 107 %bootstrap ( inpt=o r i g i n a l , seed=&j , s a m p l e s i z e=&n , r e p l i c a c a o=&nboot2 ,mu=&mu, 108 beta=&beta , i n d i c e=&j , n=&n , p=&p ) ; 109 %end ; 110 %mend cobertura ; Para a contagem dos intervalos de confiança que continham o verdadeiro valor do parâmetro de interesse, foi utilizado a proc iml do SAS. 92
Referências Bibliográficas AHMAD, K.E., MOUSTAFA, H.M., e ABD-ELRAHMAN, A.M. (1997). Approximate Bayes estimation fo mixtures of two Weibull distributions under type-2 censoring. Journal of Statistical Computation and Simulation, 58(3): 269-285. BERGER, J.M. e SUN, D.O. (1993). Bayesian analysis for the poly-Weibull distribution. Journal of the American Statistical Association, 88(424):1412-1418. BERGER, J.O.; SUN, D. Bayesian inference for a class of poly-Weibull distribuition. In: Bayesian analysis in statisticas and econometrics, essys in Honor of Arnold Zellner. Wiley: New York: Berri, D. A., Chaloner, K. M. and Geweke, J. K.,1996. 1994. COLLET, D. Modelling survival data in medical research. New York: Chapman and Hall, COX, D. R. (1972). Regression models and life-tables (with discussion) Journal of the Royal Statistical Society, B, 34: 187-220. DEVROYE, L Non-uniform random variate generation. New York: Springer-Verlag, 1986. EFRON, Bradley e TIBSHIRANI, Robert J. (1993), An introduction to the Bootstrap, vol. 57 of Monographs on Statistics and Applied Probability, Chapman and Hall, New York. GELMAN, A.; RUBIN, D. B. Inference from iterative simulation using multiple sequences. Statistical Science, 1992. GUITANY, M. E.; MALLER, R. A. Asymptotic results for exponencial mixture models with long term survivors. Statistics, 1992. GILKS, W. R. Full conditional distributions. In: Markov chain monte carlo in practice. London: Chapman and Hall, 1996. HALL, P. Theorical comparison of Bootstrap confidence intervals, Ann. Stat. 16(3) (1988) 927-985. JENG, Shuen-Lin; MEEKER, Willian Q. Asymptotic Properties of Bootstrap Likelihood Ratio Statistics for Time Censored Data, 2003. JIANG, R.; MURTHY, D. N. P. Mixture of Weibull distributions parametric characteriza- tion of failure rate function. Applied Stochastic Models and Data Analysis, 1998. JOHNSON, L. N. e Kotz, S. (1995). Continuous Univariate Distributions. John Wiley and Sons, New York. LARSON, H. Introduction to Probability Theory and Statistical Inference, 2 o ed. John Wiley, 1974. 93
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UNIVERSIDADE ESTADUAL DE MARINGÁ C
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Waldir Verissimo da Silva Junior Pr
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Sumário 1 Introdução 8 1.1 Descr
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Índice de Tabelas Tabela 4.1: Prob
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Índice de Figuras Figura 2.1: Fun
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espectivamente, n1 = 10, n2 = 20, n
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de sobrevivência. Como alternativa
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f(t, μ, β) f(t, μ, β) f(t, μ,
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S(t) = exp − β t , (2.3) µ
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2.4 Estimadores de Máxima Verossim
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Capítulo 3 Simulação Bootstrap 3
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Figura 3.1: O Procedimento Bootstra
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Capítulo 4 Aplicação 4.1 Introdu
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4.3 Análise Considerando a Distrib
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Comparando os três tipos de interv
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ProbCobertura ProbCobertura 0.80 0.
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4.3.3 Análise do Parâmetro t0.25
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Page 89 and 90: 4.6 Conclusões Neste trabalho foi
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Page 93 and 94: Utilizando o método da substituiç
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