30 Intervals

Author

Jarad Niemi

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You have a learned a number of formulas for confidence intervals. These intervals are constructed to contain the truth a certain proportion of the time, e.g. a 100(1-\(\alpha\))% interval is constructed to contain the truth parameter 100(1-\(\alpha\))% of the time.

These intervals are either exact or asymptotic. An exact interval, as the name suggests, is constructed so that the claim of “containing the true value of the parameter” is always true. The claim is still probabilistic, i.e. we expect 100(1-\(\alpha\))% of the intervals to contain the truth. Thus, we won’t always see that percentage in any given sample. An asymptotic (sometimes referred to as an approximate) interval, is constructed so the claim is true as the size of the sample goes to infinity.

30.1 Examples

Introductory statistics courses, typically teach the following confidence interval formulas.

30.1.1 Normal

Let \(Y_i\stackrel{ind}{\sim} N(\mu,\sigma^2)\) with \(\sigma\) known. A exact 100(1-\(\alpha\))% confidence interval for \(\mu\) is \[ \overline{y} \pm z_{1-\alpha/2} \sigma / \sqrt{n} \] where

\(n\) is the sample size,
\(\overline{y}\) is the sample mean, and
\(z_{1-\alpha/2}\) is the z critical value such that \(1-\alpha/2 = P(Z < z_{1-\alpha/2})\) where \(Z\) is a standard normal.

Note: The variance is known here and thus you can use a z critical value.

For example,

# Pick these
alpha <- 0.05
mu    <- 0
sigma <- 1

# Simulate data
y <- rnorm(10, mean = mu, sd = sigma)

# Calculate summary statistics
ybar <- mean(y)
n    <- length(y)
z    <- qnorm(1-alpha/2)

# Construct interval
ybar + c(-1,1) * z * sigma / sqrt(n)

[1] -0.3812873  0.8583027

Let \(Y_i\stackrel{ind}{\sim} N(\mu,\sigma^2)\) with \(\mu\) and \(\sigma^2\) both unknown. A exact 100(1-\(\alpha\))% confidence interval for \(\mu\) is \[ \overline{y} \pm t_{n-1,1-\alpha/2} s / \sqrt{n} \] where

\(n\) is the sample size,
\(\overline{y}\) is the sample mean,
\(s\) is the sample standard deviation, and
\(t_{n-1,1-\alpha/2}\) is the t critical value such that \(1-\alpha/2 = P(T_{n-1} < t_{n-1,1-\alpha/2})\) where \(T_{n-1}\) is a student \(T\) distribution with \(n-1\) degrees of freedom.

For example,

# Calculate statistics (using data above)
s <- sd(y)
t <- qt(1-alpha/2, df = n-1)

# Construct interval
ybar + c(-1,1) * t * s / sqrt(n)

[1] -0.4380182  0.9150336

As \(n\to\infty\), \(t_{n-1,1-\alpha/2} \to z_{1-\alpha/2}\) and \(s^2 \to \sigma^2\). (That is, \(s^2\) is a consistent estimator for \(\sigma^2\).) Thus, for large sample sizes, an asymptotic 100(1-\(\alpha\))% confidence interval for \(\mu\) is \[ \overline{y} \pm z_{1-\alpha/2} s / \sqrt{n} \] where we have replaced the t critical value with a z critical value.

For example

# Construct asymptotic interval
ybar + c(-1,1) * z * s / sqrt(n) # n is only 10

[1] -0.3476437  0.8246591

Note: The t interval is an exact confidence interval while this interval is an asymptotic confidence interval.

30.1.2 Binomial

Let \(X\sim Bin(n,p)\).

30.1.2.1 Wald interval

An approximate 100(1-\(\alpha\))% confidence interval for \(p\) is \[ \hat{p} \pm z_{1-\alpha/2} \sqrt{\frac{\hat{p}(1-\hat{p})}{n}} \] where \(\hat{p} = x/n\).

This is an approximate confidence interval because it is constructed based on the Central Limit Theorem (CLT) which states that, for large enough sample size, \[ \hat{p} \stackrel{\cdot}{\sim} N\left( p, \frac{\hat{p}(1-\hat{p})}{n} \right). \]

For example

# Binomial parameters
p <- 0.5
n <- 30

# Simulate data
x <- rbinom(1, size = n, prob = p)

# Construct asymptotic CI
p_hat <- x/n

p_hat + c(-1,1) * z * sqrt(p_hat*(1-p_hat)/n)

[1] 0.2881453 0.6451880

30.1.2.2 binom.test()

R actually does not implement this interval in any of its base R functions. For example,

# Default confidence interval from binom.test
binom.test(x, n)$conf.int

[1] 0.2834181 0.6567448
attr(,"conf.level")
[1] 0.95

This interval is based on the Clopper-Pearson interval. This interval finds all the values for theta that do not reject a hypothesis test (when those values are set as the null value) and constructing the interval based on those values. These confidence intervals are exact, but these intervals are often quite wide

30.1.2.3 prop.test()

Here are some alternative approximate confidence intervals in the sense that they more often contain the truth compared to the Wald interval and they are typically shorter than the Clopper-Pearson interval

prop.test(x, n)$conf.int

[1] 0.2879653 0.6536010
attr(,"conf.level")
[1] 0.95

prop.test(x, n, correct = FALSE)$conf.int

[1] 0.3023239 0.6385770
attr(,"conf.level")
[1] 0.95

These intervals are based on Wilson’s score interval with and without continuity correction. There are all approximate as they are based on a better asymptotic approximation.

There are many more approximate confidence intervals for a binomial probability.

30.2 Monte Carlo

Our goal here will be to estimate the coverage of these interval estimators. Coverage is the true probability of an interval containing the truth for repeated realizations of the data (when the model is true).

A Monte Carlo study to estimate coverage proceeds by repeating the following

simulate data,
construct an interval, and
determine whether that interval contains the truth.

The proportion of times the interval contains the truth is an estimate of its coverage and we can find the Monte Carlo standard error of that estimate by using the formula we have seen before.

# Function to calculate Monte Carlo proportion with standard error
mc_probability <- function(x) {
  p <- mean(x)
  
  return(
    c(prop = p,
      se   = sqrt(p*(1-p)/length(x)))
  )
}

30.2.1 Normal

For a normal distribution, the intervals for a normal mean are exact and both have coverage equal to the nominal level 100(1-\(\alpha\))%.

# Pick error rate
alpha <- 0.05
z     <- qnorm(1-alpha/2)

# Pick normal parameters
n     <- 10   # data sample size for each replicate
mu    <- 0
sigma <- 1

# Conduct Monte Carlo study
d <- expand.grid(
  rep = 1:1e3,   # number of Monte Carlo replicates
  n   = 1:n) |>
  
  # For each rep
  group_by(rep) |>
  
  # Simulate data
  mutate(
    y = rnorm(n(), 
              mean = mu, 
              sd   = sigma)
  ) |>
  
  # Construct CI
  summarize(
    n    = n(),
    ybar = mean(y),
    se   = sigma / sqrt(n),
    lcl  = ybar - z * se,
    ucl  = ybar + z * se,
    .groups = "drop"
  ) |>
  
  # Determine if true value is within interval
  mutate(
    within = lcl < mu & mu < ucl 
  ) 

# Visualize 
ggplot(d |> filter(rep <= 50), 
       aes(x     = rep, 
           ymin  = lcl, 
           ymax  = ucl, 
           color = within)) + 
  geom_linerange() + 
  coord_flip()

This figure visualizes the first 50 confidence intervals constructed in our simulation. We are only visualizing the first 50 because it is hard to visualize 1,000 intervals simultaneously. Some of the intervals will contain the truth (within = TRUE) and some will not (within = FALSE). On overage, intervals constructed using this procedure will contain the truth 100(1-\(\alpha\))% of the time.

Now we can calculate our Monte Carlo estimate of the coverage with its uncertainty.

# MC estimate of coverage
d |> 
  pull(within) |>
  mc_probability()

       prop          se 
0.956000000 0.006485677

The situation is very similar when the variance is unknown. The two main changes are that the interval uses 1) a t critical value and 2) the sample standard deviation.

# Normal unknown variance
t <- qt(1-alpha/2, df = n-1)

expand.grid(
  rep = 1:1e3,
  n   = 1:n) |>
  
  # Simulate data
  group_by(rep) |>
  mutate(
    y = rnorm(n(), 
              mean = mu, 
              sd = sigma)
  ) |>
  
  # Construct confidence intervals
  summarize(
    n    = n(),
    ybar = mean(y),
    se   = sd(y) / sqrt(n),
    lcl  = ybar - t * se,
    ucl  = ybar + t * se,
    .groups = "drop"
  ) |>
  
  # Determine if truth is within the interval
  mutate(
    within = lcl < mu & mu < ucl
  ) |>
  
  # MC estimate of coverage
  pull(within) |>
  mc_probability()

       prop          se 
0.953000000 0.006692608

Recall that if we replace the t critical value in the previous formula with a z critical value, we have an asymptotic interval. The coverage of this interval will depend on the data sample size. As this sample size increases, the coverage will converge to 100(1-\(\alpha\))%.

# Pick error
alpha <- 0.05
z     <- -qnorm(alpha/2)

# Pick n
n <- 10 # don't make it too big or the results will be boring

expand.grid(
  rep = 1:1e3,
  n   = 1:n) |>
  
  # Simulate data
  group_by(rep) |>
  mutate(
    y = rnorm(n(), 
              mean = mu, 
              sd   = sigma)
  ) |>
  
  # Construct CIs
  summarize(
    n    = n(),
    ybar = mean(y),
    se   = sd(y) / sqrt(n), 
    lcl = ybar - z * se, # using z here
    ucl = ybar + z * se,
    .groups = "drop"
  ) |>
  
  # Determine if true value is within interval
  mutate(
    within = lcl < mu & mu < ucl
  ) |>
  
  # MC estimate of coverage
  pull(within) |>
  mc_probability()

       prop          se 
0.918000000 0.008676174

30.2.2 Binomial

For the binomial distribution, we have a number of different intervals and we can estimate the coverage of all of these intervals. When comparing different statistical methods, it is best to use the same data.

# Pick these
alpha <- 0.05
n     <- 10
p     <- 0.78

# z critical value
z <- -qnorm(alpha/2)

# Simulate data
d <- data.frame(
  x = rbinom(1e3,      # Monte Carlo sample size
             size = n, 
             prob = p)
) 

# Construct CIs
ci <- d |>
  rowwise() |>
  mutate(
    # Wald interval
    p_hat = x/n,
    lcl = p_hat - z * sqrt(p_hat*(1-p_hat)/n),
    ucl = p_hat + z * sqrt(p_hat*(1-p_hat)/n),
    Wald = lcl < p & p < ucl,
    
    # Clopper-Pearson
    lcl = binom.test(x, n)$conf.int[1],
    ucl = binom.test(x, n)$conf.int[2],
    `Clopper-Pearson` = lcl < p & p < ucl,
    
    # Wilson's score interval
    lcl = prop.test(x, n, correct = FALSE)$conf.int[1],
    ucl = prop.test(x, n, correct = FALSE)$conf.int[2],
    `Wilson score interval` = lcl < p & p < ucl,
    
    # Wilson's score interval with continuity correction
    lcl = prop.test(x, n, correct = TRUE)$conf.int[1],
    ucl = prop.test(x, n, correct = TRUE)$conf.int[2],
    `Wilson score interval (with CC)` = lcl < p & p < ucl,
  ) |>
  select(Wald:`Wilson score interval (with CC)`)

# MC estimate of coverage
mcp <- ci |>
  pivot_longer(everything(),
               names_to  = "method", 
               values_to = "within") |>
  group_by(method) |>
  summarize(
    coverage = mean(within),
    se       = sqrt(p*(1-p)/n())
  )

mcp

# A tibble: 4 × 3
  method                          coverage     se
  <chr>                              <dbl>  <dbl>
1 Clopper-Pearson                    0.995 0.0131
2 Wald                               0.906 0.0131
3 Wilson score interval              0.949 0.0131
4 Wilson score interval (with CC)    0.949 0.0131