Math Problem Statement

Sufficient Statistic and Factorization for log normal

Solution

A sufficient statistic for a parameter of a probability distribution is a statistic that encapsulates all the information needed about that parameter from the data. The factorization theorem is a tool used to find sufficient statistics. For a log-normal distribution, we'll identify sufficient statistics by applying the factorization theorem to its likelihood function.

1. Log-Normal Distribution Overview

If XLog-Normal(μ,σ2)X \sim \text{Log-Normal}(\mu, \sigma^2), then:

  • Y=log(X)N(μ,σ2)Y = \log(X) \sim N(\mu, \sigma^2), i.e., a normal distribution with mean μ\mu and variance σ2\sigma^2.
  • The probability density function (pdf) of XX is: f(xμ,σ2)=1xσ2πexp((log(x)μ)22σ2)f(x|\mu, \sigma^2) = \frac{1}{x \sigma \sqrt{2\pi}} \exp\left(-\frac{(\log(x) - \mu)^2}{2\sigma^2}\right) for x>0x > 0.

2. Factorization Theorem

The factorization theorem states that a statistic T(X)T(X) is sufficient for parameter θ\theta if the likelihood function can be factorized as: L(θ;x)=g(T(x),θ)h(x)L(\theta; x) = g(T(x), \theta) \cdot h(x) where g(T(x),θ)g(T(x), \theta) depends on the data only through T(x)T(x) and θ\theta, and h(x)h(x) depends only on the data and not on θ\theta.

3. Likelihood Function of Log-Normal

The likelihood function for nn i.i.d. log-normal observations X1,X2,,XnX_1, X_2, \dots, X_n is: L(μ,σ2;x1,x2,,xn)=i=1nf(xiμ,σ2)L(\mu, \sigma^2; x_1, x_2, \dots, x_n) = \prod_{i=1}^{n} f(x_i | \mu, \sigma^2) Substituting the log-normal pdf: L(μ,σ2;x1,,xn)=i=1n1xiσ2πexp((log(xi)μ)22σ2)L(\mu, \sigma^2; x_1, \dots, x_n) = \prod_{i=1}^{n} \frac{1}{x_i \sigma \sqrt{2\pi}} \exp\left(-\frac{(\log(x_i) - \mu)^2}{2\sigma^2}\right) This can be rewritten as: L(μ,σ2;x1,,xn)=1(2π)n/2σni=1nxiexp(12σ2i=1n(log(xi)μ)2)L(\mu, \sigma^2; x_1, \dots, x_n) = \frac{1}{(2\pi)^{n/2} \sigma^n \prod_{i=1}^n x_i} \exp\left(-\frac{1}{2\sigma^2} \sum_{i=1}^{n} (\log(x_i) - \mu)^2\right) We aim to express this in the form of the factorization theorem.

4. Factorization and Sufficient Statistic

The likelihood function can be factorized as: L(μ,σ2;x1,,xn)=[1(2π)n/2σnexp(12σ2(i=1n(log(xi))22μi=1nlog(xi)+nμ2))]1i=1nxiL(\mu, \sigma^2; x_1, \dots, x_n) = \left[\frac{1}{(2\pi)^{n/2} \sigma^n} \exp\left(-\frac{1}{2\sigma^2} \left(\sum_{i=1}^n (\log(x_i))^2 - 2\mu \sum_{i=1}^n \log(x_i) + n\mu^2 \right)\right)\right] \cdot \frac{1}{\prod_{i=1}^n x_i} From this, we observe that the likelihood depends on the data x1,x2,,xnx_1, x_2, \dots, x_n through:

  1. i=1nlog(xi)\sum_{i=1}^n \log(x_i) (sum of log-transformed values)
  2. i=1n(log(xi))2\sum_{i=1}^n (\log(x_i))^2 (sum of squared log-transformed values)

Thus, the sufficient statistics are:

  • T1(X)=i=1nlog(Xi)T_1(X) = \sum_{i=1}^n \log(X_i)
  • T2(X)=i=1n(log(Xi))2T_2(X) = \sum_{i=1}^n (\log(X_i))^2

Conclusion: Sufficient Statistic

For the log-normal distribution with parameters μ\mu and σ2\sigma^2, the sufficient statistics, derived using the factorization theorem, are: T(X)=(i=1nlog(Xi),i=1n(log(Xi))2)T(X) = \left( \sum_{i=1}^n \log(X_i), \sum_{i=1}^n (\log(X_i))^2 \right) These capture all the necessary information about μ\mu and σ2\sigma^2 from the data.

Would you like more details on the application of the factorization theorem? Here are 5 related questions for further exploration:

  1. How does the log-normal distribution relate to the normal distribution?
  2. What is the interpretation of the sufficient statistic in the context of parameter estimation?
  3. Can you explain the importance of minimal sufficient statistics?
  4. How would the Maximum Likelihood Estimators (MLE) be derived for the log-normal distribution?
  5. What is the role of exponential families in the context of sufficient statistics?

Tip: Sufficient statistics allow for data reduction without losing information about the parameter, making them key for efficient estimation.

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Math Problem Analysis

Mathematical Concepts

Probability Theory
Log-Normal Distribution
Sufficient Statistic

Formulas

Log-Normal PDF: f(x|μ, σ^2) = 1 / (x σ √(2π)) exp(-(log(x) - μ)^2 / (2σ^2))
Factorization Theorem: L(θ; x) = g(T(x), θ) · h(x)
Likelihood Function: L(μ, σ^2; x_1, ..., x_n) = ∏[1/(x_i σ √(2π)) exp(-(log(x_i) - μ)^2 / (2σ^2))]

Theorems

Factorization Theorem
Sufficient Statistic

Suitable Grade Level

University Level - Advanced Statistics