Description Usage Arguments Value Author(s) References See Also Examples
Performs the EM algorithm for Birnbaum-Saunders model based on Skew-Normal distribution.
1 2 |
ti |
the response vector of dimension n where n is the total of observations. |
alpha,beta,delta |
an numeric of initial estimates. |
loglik |
showvalue of the log-likelihood (V) or not (F). |
accuracy |
the convergence maximum error. |
show.envelope |
TRUE or FALSE. Indicates if envelope graph should be built for the fitted model. Default is FALSE. |
iter.max |
The maximum number of iterations of the EM algorithm |
The function returns a list with 11 elements detailed as
iter |
number of iterations. |
alpha |
estimated shape parameter. |
beta |
estimated scale parameter. |
lambda |
estimate skewness parameter. |
SE |
Standard Error estimates. |
table |
Table containing the inference for the estimated parameters. |
loglik |
Log-likelihood value. |
AIC |
Akaike information criterion. |
BIC |
Bayesian information criterion. |
HQC |
Hannan-Quinn information criterion. |
time |
processing time. |
Rocio Maehara rmaeharaa@gmail.com and Luis Benites lbenitesanchez@gmail.com
Vilca, Filidor; Santana, L. R.; Leiva, Victor; Balakrishnan, N. (2011). Estimation of extreme percentiles in Birnbaum Saunders distributions. Computational Statistics & Data Analysis (Print), 55, 1665-1678.
Santana, Lucia; Vilca, Filidor; Leiva, Victor (2011). Influence analysis in skew-Birnbaum Saunders regression models and applications. Journal of Applied Statistics, 38, 1633-1649.
bssn
, EMbssn
, momentsbssn
, ozone
, reliabilitybssn
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | ## Not run:
#Using the ozone data
data(ozone)
attach(ozone)
#################################
#The model
ti <- dailyozonelevel
#Initial values for the parameters
initial <- mmmeth(ti)
alpha0 <- initial$alpha0ini
beta0 <- initial$beta0init
lambda0 <- 0
delta0 <- lambda0/sqrt(1+lambda0^2)
#Estimated parameters of the model (by default)
est_param <- EMbssn(ti,alpha0,beta0,delta0,loglik=T,
accuracy = 1e-8,show.envelope = "TRUE", iter.max=500)
#ML estimates
alpha <- est_param$res$alpha
beta <- est_param$res$beta
lambda <- est_param$res$lambda
#########################################
#A simple output example
---------------------------------------------------------
Birnbaum-Saunders model based on Skew-Normal distribution
---------------------------------------------------------
Observations = 116
-----------
Estimates
-----------
Estimate Std. Error z value Pr(>|z|)
alpha 1.26014 0.23673 5.32311 0.00000
beta 14.65730 4.01984 3.64624 0.00027
lambda 1.06277 0.54305 1.95706 0.05034
------------------------
Model selection criteria
------------------------
Loglik AIC BIC HQC
Value -542.768 4.705 4.741 4.719
-------
Details
-------
Iterations = 415
Processing time = 0.4283214 secs
Convergence = TRUE
## End(Not run)
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