estimates {bvpa} R Documentation

## Estimation of Block-Basu Bivariate Pareto (BBBVPA) distribution

### Description

Parameters estimation of BBBVPA distribution.

### Usage

estimates(
I,
s1.int,
s2.int,
a0.int,
a1.int,
a2.int,
tol.est = 1e-05,
MxIter.no = 2000,
rate = 1e-04,
condition = "log.L"
)


### Arguments

 I bivariate observations. s1.int initial choice of \sigma_1. s2.int initial choice of \sigma_2. a0.int initial choice of \alpha_0. a1.int initial choice of \alpha_1. a2.int initial choice of \alpha_2. tol.est convergence tolerance, 0.00001 (default). MxIter.no maximum number of iterations, 2000 (default). rate step size or learning rate for gradient descent, 0.0001 (default). condition convergence criterion, "log.L" (default) and "p.logL".

### Value

object of class "bbbvpa", a list consisting of

 mu1, mu2, sigma1, sigma2, alpha0, alpha1, alpha2, iter.no estimates of parameters and number of iteration. data the supplied data I.

### Author(s)

Biplab Paul <paul.biplab497@gmail.com> and Arabin Kumar Dey <arabin@iitg.ac.in>

### Examples


data(precipitation)
data <- as.vector(precipitation[,2])
data[is.na(data)]<-0
n <- length(data)
# Construct the three-dimensional data set
data3d <- function(data){
u <- 12
Y <- c()
indx <- indx1 <- indx2 <- indx3 <- 0
r <- 5
i <- 2
while(i < n){
i <- i + 1
if(data[i] > u || sum(data[(i-1):i]) > u || sum(data[(i-2):i]) > u){
if(data[i] > u){imax <- i}
if(sum(data[(i-1):i]) > u){imax <- i - 3 + which(data[(i-1):i] == max(data[(i-1):i]))[1]}
if(sum(data[(i-2):i]) > u){imax <- i - 3 + which(data[(i-2):i] == max(data[(i-2):i]))[1]}
if(max(indx) > (imax-r)){
cluster <- data[(max(indx)+3):(imax+r)]
} else{
cluster <- data[(imax-r):(imax+r)]
}
cluster2 <- sapply(c(1:(length(cluster)-1)), function(j) sum(cluster[j:(j+1)]))
cluster3 <- sapply(c(1:(length(cluster)-2)), function(j) sum(cluster[j:(j+2)]))
indx1 <- append(indx1,imax-r-1+which(cluster==max(cluster))[1])
indx2 <- append(indx2,imax-r-1+which(cluster2==max(cluster2)))
indx3 <- append(indx3,imax-r-1+which(cluster3==max(cluster3)))
Y <- rbind(Y, c(max(cluster),max(cluster2),max(cluster3)))
indx <- append(indx,imax)
i <- i + r
}
}
return(Y)
}
I <- data3d(data)[,c(1,3)]
iniz <- intliz(I)
iniz
est <- estimates(I, iniz[1], iniz[2], iniz[3], iniz[4], iniz[5])
est[-9]
param.boot(I, iniz[1], iniz[2], iniz[3], iniz[4], iniz[5])
conf.intv(est)



[Package bvpa version 1.0.0 Index]