---
title: "Estimation of Finite Population Total under Complex Sampling Design viz. SRSWOR"
author: "Nobin Ch Paul"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
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%\VignetteIndexEntry{Estimation of Finite Population Total under Complex Sampling Design viz. SRSWOR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## 1. Introduction
In survey sampling, a **population** is a well-defined set of identifiable and observable units relevant to a survey’s objective. Information about its parameters can be obtained through a Census (complete enumeration) or by selecting a representative sample using sampling methods. Populations may be finite or infinite, and sampling is performed on defined units listed in a sampling frame.
**Simple Random Sampling Without Replacement (SRSWOR)** is a fundamental sampling design, where every subset of size \( n \) from a population of size \( N \) has equal chance of being selected, with no repeated selections.
Two primary paradigms in inference are:
- **Design-based**: Population values are fixed; randomness comes from the sampling process. One example is the **Horvitz-Thompson (HT) estimator**
- **Model-based**: Population values are random variables modeled using probability distributions.
In both, **auxiliary information** can improve estimation accuracy. Common estimators leveraging this include:
- **Ratio estimator**
- **Regression estimator**
This study uses **Monte Carlo simulation** to compare their performance in estimating the **population total** under SRSWOR sampling design.
## 2. Estimators and Their Formulas
Let:
- \( N \): Population size
- \( n \): Sample size
- \( y_i \): Value of study variable for unit \( i \)
- \( x_i \): Value of auxiliary variable for unit \( i \)
- \( \bar{y}_s, \bar{x}_s \): Sample means of \( y \) and \( x \)
- \( \bar{Y}, \bar{X} \): Population means of \( y \) and \( x \)
- \( X = \sum_{i=1}^N x_i \): Known total of auxiliary variable
### 2.1. Horvitz-Thompson Estimator
The Horvitz-Thompson (HT) estimator for a finite population total is given by:
\[
\hat{Y}_{HT} = \sum_{i \in s} \frac{y_i}{\pi_i}
\]
where
- \( s \) is the sample drawn from the population,
- \( y_i \) is the observed value of the variable of interest for unit \( i \),
- \( \pi_i \) is the inclusion probability of unit \( i \).
Under SRSWOR, formula can be written as
\[
\hat{Y}_{HT} = N \cdot \bar{y}_s
\]
This estimator is **unbiased** under SRSWOR.
### 2.2. Ratio Estimator
\[
\hat{Y}_{R} = X \cdot \frac{\bar{y}_s}{\bar{x}_s}
\]
Efficient when \( y \) and \( x \) are **positively correlated** and the relationship passes through the origin.
### 2.3. Regression Estimator
\[
\hat{Y}_{reg} = N \left[\bar{y}_s + b(\bar{X} - \bar{x}_s)\right]
\]
where
\[
b = \frac{\sum (x_i - \bar{x}_s)(y_i - \bar{y}_s)}{\sum (x_i - \bar{x}_s)^2}
\]
Performs well under **linear relationship** between \( x \) and \( y \).
## 3. Performance Metrics Used in Simulation
| Metric | Description |
|---------------|-------------|
| `Est_Total` | Average estimated total over all simulations |
| `RB` | **Relative Bias (%)**: \( \frac{\text{Mean Estimate} - \text{True Total}}{\text{True Total}} \times 100 \) |
| `RRMSE` | **Relative Root Mean Square Error (%)**: \( \frac{\sqrt{\text{MSE}}}{\text{True Total}} \times 100 \) |
| `Skewness` | Asymmetry of estimator distribution |
| `Kurtosis` | Peakedness of estimator distribution |
| `CR` | **Coverage Rate**: Proportion of times the 95% CI includes the true total |
| `For_var` | Theoretical (design-based) variance |
| `Emp_var` | Empirical variance from simulation |
| `Est_var` | Mean of estimated variances across simulations |
| `perc_CV` | Coefficient of Variation: \( \frac{SD}{Mean} \times 100 \) |
## 4. Monte Carlo Simulation Framework
A population is synthetically generated with size \( N = 1000 \). An auxiliary variable \( x \) is drawn from a normal distribution, and the study variable \( y \) is linearly related to \( x \) with added noise.
A sample of size \( n = 100 \) is drawn without replacement from the population, and using this sample, an estimate finite population total of all estimators has been calculated. This procedure was repeated independently, say 1000 times (simulation number).Measures like percentage relative bias (%RB) and percentage relative root mean square error (%RRMSE) have been used to compare the performance of the estimators like HT, Ratio and Regression estimators.
## 5. Results Summary
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Function: `survey_sim_est`
This function performs a simulation study to evaluate the performance of three survey estimators: Horvitz-Thompson (HT), Ratio, and Regression estimators. It generates statistics like Relative Bias (RB), Relative Root Mean Square Error (RRMSE), Coefficient of Variation (CV), Skewness, Kurtosis, and Coverage Probability over a given number of simulations.
### Function Definition
```{r}
survey_sim_est <- function(Y, X, n = 40, SimNo = 500, seed = 123) {
if (length(Y) != length(X)) stop("Y and X must be of the same length.")
set.seed(seed)
N <- length(Y)
Y.total <- sum(Y)
X.total <- sum(X)
True.Total <- rep(Y.total, SimNo)
data <- data.frame(ID = 1:N, Y = Y, X = X)
est1 <- est2 <- est3 <- var_est1 <- var_est2 <- var_est3 <- numeric(SimNo)
for (h in 1:SimNo) {
set.seed(h)
sa <- sample(1:N, n, replace = FALSE)
samp <- data[sa, ]
di <- rep(N / n, n)
y_samp <- samp$Y
x_samp <- samp$X
s2_y <- var(y_samp)
s2_x <- var(x_samp)
rho_hat <- cor(y_samp, x_samp)
R_hat <- mean(y_samp) / mean(x_samp)
# HT Estimator
est1[h] <- sum(di * y_samp)
var_est1[h] <- N^2 * ((1 / n) - (1 / N)) * s2_y
# Ratio Estimator
ratio1 <- di * y_samp
ratio2 <- di * x_samp
est2[h] <- (sum(ratio1) / sum(ratio2)) * X.total
var_est2[h] <- N^2 * ((1 / n) - (1 / N)) * (s2_y + (R_hat^2 * s2_x) - (2 * R_hat * rho_hat * sqrt(s2_y * s2_x)))
# Regression Estimator
b_hat <- rho_hat * sqrt(s2_y) / sqrt(s2_x)
est3[h] <- est1[h] + b_hat * (X.total - sum(ratio2))
var_est3[h] <- N^2 * ((1 / n) - (1 / N)) * s2_y * (1 - rho_hat^2)
}
# Population-level parameters
pop_s2_y <- var(Y)
pop_s2_x <- var(X)
R_pop <- mean(Y) / mean(X)
rho_pop <- cor(Y, X)
var_HT <- N^2 * ((1 / n) - (1 / N)) * pop_s2_y
var_ratio <- N^2 * ((1 / n) - (1 / N)) * (pop_s2_y + R_pop^2 * pop_s2_x - 2 * R_pop * rho_pop * sqrt(pop_s2_y * pop_s2_x))
var_reg <- N^2 * ((1 / n) - (1 / N)) * pop_s2_y * (1 - rho_pop^2)
# Evaluation metrics
RB <- 100 * colMeans(cbind(est1, est2, est3) - True.Total) / mean(True.Total)
RRMSE <- 100 * sqrt(colMeans((cbind(est1, est2, est3) - True.Total)^2)) / mean(True.Total)
CV <- 100 * apply(cbind(est1, est2, est3), 2, sd) / colMeans(cbind(est1, est2, est3))
skew <- apply(cbind(est1, est2, est3), 2, moments::skewness)
kurt <- apply(cbind(est1, est2, est3), 2, moments::kurtosis)
emp_var <- apply(cbind(est1, est2, est3), 2, var)
est_var <- c(mean(var_est1), mean(var_est2), mean(var_est3))
coverage <- colMeans(abs(cbind(est1, est2, est3) - True.Total) <= 1.96 * sqrt(cbind(var_est1, var_est2, var_est3)))
result <- data.frame(
Est_Total = c(mean(est1), mean(est2), mean(est3)),
RB = RB,
RRMSE = RRMSE,
Skewness = skew,
Kurtosis = kurt,
Coverage = coverage,
PopVar = c(var_HT, var_ratio, var_reg),
EmpVar = emp_var,
EstVar = est_var,
CV = CV
)
rownames(result) <- c("HT", "Ratio", "Regression")
return(round(result, 3))
}
```
### Example Usage
```{r example, message=FALSE}
set.seed(123)
N <- 1000
X <- rnorm(N, mean = 50, sd = 10)
Y <- 3 + 1.5 * X + rnorm(N, mean = 0, sd = 10)
result <- survey_sim_est(Y = Y, X = X, n = 50, SimNo = 100)
print(result)
```
## 6. Interpretation of Results
- The **Horvitz-Thompson estimator** is **unbiased** but may exhibit higher variance.
- The **Ratio estimator** is more **efficient** when the relationship between \( y \) and \( x \) is strong and passes through origin.
- The **Regression estimator** generally shows **lowest RRMSE**.
**Coverage Rate** near 95% indicates good interval performance, while low `RB` and `RRMSE` signify estimators unbiasedness and precision.
## 7. Conclusion
This simulation illustrates the benefits of using **auxiliary information** in improving the estimation of finite population totals. While the Horvitz-Thompson estimator offers unbiasedness under design-based framework, the ratio and regression estimators leverage the auxiliary variable to reduce variance significantly when assumptions are met.
```R
*Launch the Shiny app
library(surveySimR)
surveySimR::run_survey_sim_app()
* Alternative way of launching shiny app
shiny::runApp(system.file("shiny", package = "surveySimR"))
```
## References
- Cochran, W. G. (1977). *Sampling Techniques*. 3rd Edition. Wiley.
- Singh, D. and Chaudhary, F.S. (1986). *Theory and Analysis of Sample Survey Designs*. John Wiley & Sons, Inc.
- Sukhatme, P.V., Sukhatme, B.V., Sukhatme, S. and Asok, C. (1984). *Sampling Theory of Surveys with Applications*. Iowa State University Press and Indian Society of Agricultural Statistics.
- Särndal, C.-E., Swensson, B., & Wretman, J. (1992). *Model Assisted Survey Sampling*. Springer.