39  Estimators

Author

Jarad Niemi

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Recall that estimators are statistics that attempt to estimate a population parameter. We are generally assuming here that we have a simple random sample from the population and we are interested in estimating a mean or proportion from that population.

39.1 Examples

39.1.1 Binomial

Let \(X \sim Bin(n,p)\). The MLE for \(p\) is \[ \hat{p} = \frac{x}{n}. \]

39.1.2 Normal

Let \(Y_i \stackrel{ind}{\sim} N(\mu,\sigma^2)\).

39.1.2.1 Mean

The maximum likelihood estimator (MLE) for \(\mu\) is \[ \hat\mu = \overline{y}. \] This also happens to be the least squares estimator and the Bayes estimator (assuming the standard default prior).

39.1.2.2 Variance

The MLE for \(\sigma^2\) is \[ \hat\sigma^2_{MLE} = \frac{1}{n} \sum_{i=1}^n (y_i-\overline{y})^2 \] while the sample variance is \[ s^2 = \frac{1}{n-1} \sum_{i=1}^n (y_i-\overline{y})^2. \] So why might we prefer one of these estimators over another?

39.1.3 Regression

The regression model (in multivariate normal form) is \[ Y \sim N(X\beta,\sigma^2\mathrm{I}). \] The MLE, least squares, and default Bayes estimator for \(\beta\) is \[ \hat\beta = (X^\top X)^{-1}X^\top y. \]

39.2 Properties

There are a variety of reasons we might use to choose an estimator. We might appreciate the universality of the maximum likelihood estimator or the ability to incorporate prior information in Bayes estimator. Here we will talk about some properties of estimators, namely:

  • bias
  • variance
  • mean squared error
  • consistency

For the following \(\theta\) is the population parameter we are trying to estimate and \(\hat{\theta}\) is the estimator of \(\theta\).

39.2.1 Bias

The bias of an estimator is the expectation of the estimator minus the true value of the estimator. \[ \mbox{Bias}\left[\hat{\theta}\right] = E\left[\hat{\theta} - \theta\right] = E\left[\hat{\theta}\right] - \theta. \]

An estimator is unbiased when the bias is 0 or, equivalently, its expectation is equal to the true value.

For example,

  • \(E\left[\hat{p}_{MLE}\right] = p\)
  • \(E\left[\hat\mu\right] = \mu\)
  • \(E\left[s^2\right] = \sigma^2\)

indicate that all of these estimators are unbiased.

In contrast

\[E[\hat\sigma^2_{MLE}] = \frac{n-1}{n}\sigma^2\]

and thus the bias is

\[Bias\left[\hat\sigma^2_{MLE}\right] = E\left[\hat\sigma^2_{MLE}\right] - \sigma^2 = \frac{1}{n}.\]

Clearly this bias diminishes as \(n\to \infty\).

39.2.2 Variance

The variance of an estimator is just the variance of the statistic: \[ Var\left[\hat{\theta}\right] = E\left[(\hat{\theta} - E[\hat{\theta}])^2\right]. \]

For example, the MLE for a binomial probability of success is

  • \(Var\left[\hat{p}\right] = E\left[(\hat{p} - E[\hat{p}])^2\right] = \frac{p(1-p)}{n}\)
  • \(Var\left[\hat\mu\right] = E\left[(\hat\mu - E[\hat\mu])^2\right] = \sigma^2/n\)

Notice how both of these decrease as \(n\to \infty\).

39.2.3 Mean squared error

The mean squared error (MSE) of an estimator is the expectation of the squared difference between the estimator and the truth. This turns out to have a simple formula in terms of the bias and variance.

\[MSE\left[\hat{\theta}\right] = E\left[\left(\hat{\theta} - \theta\right)^2\right] = Var\left[\hat{\theta}\right] + Bias\left[\hat{\theta}\right]^2 \]

In our examples, we have

  • \(MSE\left[\hat{p}\right] = \frac{p(1-p)}{n} + 0^2\)
  • \(MSE\left[\hat\mu\right] = \sigma^2/n + 0^2\)

For unbiased estimators, the MSE is the same as the variance which is why we talk about minimum variance unbiased estimators (MVUE).

For biased estimators, the formula is a bit more interesting and can result in estimators that are better than MVUE in regard to MSE.

39.2.4 Consistency

An estimator is consistent if it gets closer to the true value in probability as the sample size gets larger. Specifically \[ \lim_{n\to\infty} P(|\hat{\theta}-\theta| > \epsilon) = 0 \] for all \(\epsilon>0\).

For unbiased estimators, the estimator is consistent if the variance converges to zero as the sample size increases. For biased estimators, the estimator is consistent if both the variance and the bias converge to zero as the sample size increases.

Every estimator discussed above is consistent.

39.3 Monte Carlo Evaluation

Let’s use Monte Carlo to evaluate these estimators. We will need to obtain many samples to estimate bias and variance. Recall the following function from the Monte Carlo basics page.

# Function to calculate Monte Carlo expectation with standard error
mc_expectation <- function(x) {
  return(
    c(mean = mean(x),
      se   = sd(x)/sqrt(length(x)))
  )
}

39.3.1 Binomial - maximum likelihood estimator

Recall that, if \(X\sim Bin(n,p)\), then the MLE for the probability of success in a binomial is \[ \hat{p}_{MLE} = x/n. \]

39.3.1.1 Bias

# Binomial example
n <- 10
p <- 0.3

x <- rbinom(1e3, size = n, prob = p)
theta_hat = x / n
# Sampling distribution for binomial MLE
ggplot() + 
  geom_histogram(
    mapping = aes(x = theta_hat,
                  y = after_stat(density)), 
    bins    = 30) +
  geom_vline(
    xintercept = p, 
    col        = 'red') +
  labs(
    x = expression(hat(theta)[MLE]),
    title = "Sampling distribution for sample proportion")

# Binomial MLE expectations
mc_expectation(  theta_hat )                      # mean
       mean          se 
0.292800000 0.004750866 
mc_expectation(  theta_hat - p )                  # bias
        mean           se 
-0.007200000  0.004750866 
mc_expectation( (theta_hat - mean(theta_hat))^2 ) # variance
        mean           se 
0.0225481600 0.0009450269 
mc_expectation( (theta_hat - p              )^2 ) # mean-squared error
        mean           se 
0.0226000000 0.0009297873

39.3.1.2 Consistency

Consistency is evaluated through a probability as the number of samples tends to infinity. Thus, we will need to evaluate a collection of probabilities.

For MLE for a binomial distribution with unknown probability of success, we can compute the probability that the MLE is outside of \(\epsilon\) of the true value. For consistency to hold, the limit of this probability must be 0 for any \(\epsilon>0\). But to visualize results, we need to make \(\epsilon\) relatively large.

# Pick these
epsilon <- 0.01
p       <- 0.3

# Set up sequence of values of n
n_seq <- floor(10^seq(1, 5, length = 10))
p_seq <- numeric(length(n_seq))

# Conduct Monte Carlo study 
for (i in seq_along(n_seq)) {
  # Simulate binomial random variables
  x <- rbinom(1e3,             # Determines accuracy of Monte Carlo estimates of the probabilities
              size = n_seq[i], # This is the n involved in the limit of the consistency definition
              prob = p)
  
  # Calculate probability of being 
  p_seq[i] <- mean(abs(x/n_seq[i] - p) > epsilon)
}

ggplot(data.frame(n = n_seq, 
                  p = p_seq),
       aes(x = n, 
           y = p_seq)) + 
  geom_point() + 
  geom_line() +
  labs(x = "Sample size", y = "Probability",
       title = "Consistency of sample proportion")

39.3.2 Binomial - default Bayesian

A default Bayesian analysis for an unknown binomial probability is \[ \hat{p}_{Bayes} = \frac{0.5 + x}{1 + n} \]

39.3.2.1 Bias

# Pick these
n <- 10
p <- 0.3

# Monte Carlo simulations
x <- rbinom(1e3, size = n, prob = p)
theta_hat <- (0.5 + x) / (1 + n)

Plot the Bayes estimator.

ggplot() + 
  geom_histogram(mapping = aes(x = theta_hat), bins = 30) +
  geom_vline(xintercept = p, col = 'red') +
  labs(title="Sampling distribution for sample proportion")

mc_expectation( theta_hat                       ) # mean
       mean          se 
0.315181818 0.004256355 
mc_expectation( theta_hat - p                   ) # bias
       mean          se 
0.015181818 0.004256355 
mc_expectation( (theta_hat - mean(theta_hat))^2 ) # variance
        mean           se 
0.0180984380 0.0007949889 
mc_expectation( (theta_hat - p)^2 )               # MSE
        mean           se 
0.0183289256 0.0008372276

39.3.2.2 Consistency

# Consistency of Bayes estimator
epsilon <- 0.01
n_seq <- floor(10^seq(1, 5, length = 10))
p_seq <- numeric(length(n_seq))

for (i in seq_along(n_seq)) {
  x <- rbinom(1e3, size = n_seq[i], prob = p)
  p_seq[i] <- mean(abs(x/n_seq[i] - p) > epsilon)
}

ggplot(data.frame(n = n_seq, 
                  p = p_seq),
       aes(x = n, 
           y = p_seq)) + 
  geom_point() + 
  geom_line() +
  labs(x = "Sample size", y = "Probability",
       title = "Consistency of sample proportion")

39.3.3 Normal

39.3.3.1 Bias

# Pick these
n <- 10
m <- 0
s <- 1

d <- expand.grid(rep = 1:1e3,
                n = 1:n) |>
  mutate(y = rnorm(n())) |>
  group_by(rep) |>
  summarize(
    n    = n(),
    ybar = mean(y),
    sd   = sd(y)
  )
ggplot(d, aes(x = ybar)) + 
  geom_histogram(bins = 30) + 
  geom_vline(xintercept = m, color = "red") +
  labs(x = "Sample mean", 
       title = "Sampling distribution of sample mean")

# Monte Carlo expectations
mc_expectation(  d$ybar                    ) # mean
        mean           se 
-0.004816330  0.009990885 
mc_expectation(  d$ybar - m                ) # bias
        mean           se 
-0.004816330  0.009990885 
mc_expectation( (d$ybar - mean(d$ybar) )^2 ) # variance
       mean          se 
0.099717957 0.004557586 
mc_expectation( (d$ybar - m            )^2 ) # MSE
       mean          se 
0.099741154 0.004559577

39.3.3.2 Consistency

epsilon <- 0.01
n_seq <- floor(10^seq(1, 5, length = 10))
p_seq <- numeric(length(n_seq))

for (i in seq_along(n_seq)) {
  xbar <- replicate(1e3, {       # replicate is another looping option
    mean( rnorm(n_seq[i], m, s))
  })
  p_seq[i] <- mean( abs(xbar - m) > epsilon)
}
   
ggplot(data.frame(n = n_seq, p = p_seq), 
       aes(x = n, y = p)) +
  geom_line() +
  geom_point() + 
  labs(x = "Sample size", y = "Probability",
       title = "Consistency of sample mean")