DE_OEM {DOEM}

The DE-OEM algorithm replaces E-step with stochastic step in distributed manner, which is used to solve the parameter estimation of Poisson mixture model.

Description

The DE-OEM algorithm replaces E-step with stochastic step in distributed manner, which is used to solve the parameter estimation of Poisson mixture model.

Usage

DE_OEM(y, M, K, seed, alpha0, lambda0, a, b)

Arguments

y

is a vector

M

is the number of subsets

K

is the number of Poisson distribution

seed

is the recommended way to specify seeds

alpha0

is the initial value of the mixing weight

lambda0

is the initial value of the mean

a

represents the power of the reciprocal of the step size

b

indicates that the M-step is not implemented for the first b data points

Value

DE_OEMtime,DE_OEMalpha,DE_OEMlambda

Examples

library(stats)
set.seed(637351)
K=5 
alpha1=c(rep(1/K,K)) 
lambda1=c(1,2,3,4,5) 
n=300 
U=sample(c(1:n),n,replace=FALSE)
y= c(rep(0,n)) 
for(i in 1:n){
if(U[i]<=0.2*n){
y[i] = rpois(1,lambda1[1])} 
else if(U[i]>0.2*n & U[i]<=0.4*n){
y[i] = rpois(1,lambda1[2])} 
else if(U[i]>0.4*n & U[i]<=0.6*n){
y[i] = rpois(1,lambda1[3])} 
else if(U[i]>0.6*n & U[i]<=0.8*n){
y[i] = rpois(1,lambda1[4])}
else if(U[i]>0.8*n ){
y[i] = rpois(1,lambda1[5])} 
}
M=5
seed=637351
set.seed(123) 
e=sample(c(1:n),K)
alpha0=e/sum(e)
lambda0=c(1.5,2.5,3.5,4.5,5.5)
a=1
b=5
DE_OEM(y,M,K,seed,alpha0,lambda0,a,b)