 # 1. Write codes to check on the property of the following vsriance e...

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1. Write codes to check on the property of the following vsriance estimators 1/ var(fr) E 1 - Iti y7 + EE Tij - Hirt yiy; = IT; 2 i=1 i=1 ji JiToj Tij y V 1 1 I 1 = E 2 y² +2 2 E yiy; IT Iti i=1 i=1j>i TITI Tij And The alternative variance estimator is N - 11 57 2 var(fr) = t (6.8) N V Use data trees when trees\$Hight is variable of interest and trees\$Girth is the vaiable used for size. Write done your comments on the results.

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var_fun_alt <- function(y, x){
# Calculating probabilities ie. p
p = x/sum(x)

# Finding unique values from x
v = length(unique(x))

# Finding total number of observations
N = length(y)

# Taking v samples from the overall data
sa = sample(1:N, v, replace = TRUE, prob =p )

# Taking pi only of samples
p_sa = p[sa]

# Taking y values of samples
y_sa = y[sa]

# Calculating pi ie. pi = 1 - (1-p)^n
pii = 1 - (1 - p_sa)^v

# Unbiased estimator of tau
tauh = sum(y[sa]/p[sa])

# Calculating t = v*y/pi
t = v * y_sa/pii

# Finding st^2 using the formula i.e. [1/(v-1)]* sum((t-pi)^2)
s_t2 = (1/(v-1)) * sum((t-pii)^2)

# The alternative variance estimator is
vhat = ((N-v)/N) * s_t2/v

# Returning results
res=c(est=tauh,varh=vhat)
res
}

var_fun <- function(y, x) {

# Calculating probabilities ie. pi
p = x/sum(x)

# Finding unique values from x
v = length(unique(x))

# Finding total number of observations
N = length(y)

# Taking v samples from the overall data
sa = sample(1:N, v, replace = TRUE, prob =p )

# Taking pi only of samples
p_sa = p[sa]

# Taking y values of samples
y_sa = y[sa]

# Calculating pi ie. pi = 1 - (1-p)^n
pii = 1 - (1 - p_sa...

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