Other univariate distributions related to the normal


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  • The following topics will be covered in this lecture:
    • The \( \chi^2 \) distribution
    • The student-t distribution
    • The F-distribution

The \( \chi^2 \) distribution

  • While the normal distribution is frequently applied to describe the underlying distribution of a statistical experiment, asymptotic test statistics are often based on a transformation of a (non-) normal rv.

  • To get a better understanding of these tests, it will be helpful to study the \( \chi^2 \), t- and F-distributions, and their relations with the normal one.

  • We will begin with the \( \chi^2 \) distribution, describing the sum of the squares of independent standard normal rvs.

  • If \( Z_i \sim N(0, 1) \), for \( i = 1, \cdots, n \) are independent, then the rv \( X \) given by

    \[ \begin{align} X = \sum_{i=1}^n Z_i \sim \chi^2_n \end{align} \] the \( \chi^2_n \) distribution in \( n \) total degrees of freedom.

  • This distribution is of particular interest since it describes the distribution of a sample variance as an unbiased estimator varying about the true parameter.

The \( \chi^2 \) distribution

  • The pdf of the \( \chi^2 \) distribution is \[ \begin{align} f(z,n) = \frac{2^{-\frac{n}{2}} z^{\frac{n}{2} - 2}exp\left(-\frac{z}{2}\right)}{\Gamma\left(\frac{n}{2}\right)}, \end{align} \]
  • where \( \Gamma(k) \) is the classical “gamma function” given as, \[ \begin{align} \Gamma(z)=\int_0^\infty t^{z-1}\exp\left(-t\right)\mathrm{d}t. \end{align} \]
  • The cdf of the χ2 distribution is \[ \begin{align} F(z,n)= \frac{\Gamma_z\left(\frac{z}{2}, \frac{z}{2}\right)}{\Gamma\left(\frac{n}{2}\right)} \end{align} \] where \[ \Gamma_z(\alpha) = \int_0^z t^{\alpha -1} \exp\left(-t\right)\mathrm{d}t \] is the incomplete gamma function.

The \( \chi^2 \) distribution

  • The standard implemented functions for the \( \chi^2 \) distribution are as follow:

    • dchisq(x, df) is the pdf;
    • pchisq(q, df) is the cdf;
    • qchisq(p, df) is the quantile;
    • rchisq(n, df) is the function for generating a sample.
  • Same as for other distributions, if log = TRUE in dchisq function, then log density is computed, which is useful for maximum likelihood estimation.

  • Similar to the functions for the t and F distributions, all the functions also have the parameter ncp which is the non-negative parameter of non-centrality,

    • this refers to when this rv is constructed from Gaussian rvs with non-zero expectations.

The \( \chi^2 \) distribution

  • In the below we plot how the pdf of the \( \chi^2 \) changes for higher numbers of degrees of freedom.

    • These are varied as n=5, n=10, n=15 and n=25.
par(cex = 2.0, mar = c(5, 4, 4, 2) + 0.3)
z = seq(0, 50, length = 300)
df = c(5, 10, 15, 25)
colors = c("black", "red", "green", "blue")
plot(z, dchisq(z,  df[1]),  type = "l", xlab = "z", ylab = "pdf")
for (i in 2:4) { lines(z, dchisq(z, df[i]), col = colors[i])}

plot of chunk unnamed-chunk-1

  • In general, the \( \chi^2 \) pdf is bell-shaped and shifts to the right-hand side for greater numbers of degrees of freedom, becoming more symmetric.

The \( \chi^2 \) distribution

  • There are two special cases, namely n = 1 and n = 2.
par(cex = 2.0, mar = c(5, 4, 4, 2) + 0.3)
z = seq(0, 50, length = 300)
m = c(1, 2)
plot(z, dchisq(z, m[1]), type = "l", xlab = "z", ylab = "pdf", xlim = c(0, 10), xaxs = "i", yaxs = "i")
lines(z, dchisq(z, m[2]), col = "blue")

plot of chunk unnamed-chunk-2

  • In the first case, the vertical axis is an asymptote and the distribution is not defined at 0.

  • In the second case, the curve steadily decreases from the value 0.5

The \( \chi^2 \) distribution

  • Respectively, using the pchisq function we can plot the cdf for each number of degrees of freedom n=5, n=10, n=15 and n=25 as
par(cex = 2.0, mar =  c(5, 4, 4, 2) + 0.3)
z = seq(0, 50, length = 300)
df = c(5, 10, 15, 25)
colors = c("red", "green", "blue")
plot(z, pchisq(z,  df[1]),  type = "l", xlab = "z", ylab = "cdf")
for (i in 2:4) { lines(z, pchisq(z, df[i]), col = colors[i-1]) }

plot of chunk unnamed-chunk-3

  • A distinctive feature of \( \chi^2 \) is that it is positive, due to the fact that it represents a sum of squared values.

  • The expectation and variance are both given by,

    \[ \begin{align} \mathbb{E}\left[X\right] = n & & \mathrm{var}\left(X\right) = 2n \end{align} \]

Student's t-distribution