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 , then:
- , i.e., a normal distribution with mean and variance .
- The probability density function (pdf) of is: for .
2. Factorization Theorem
The factorization theorem states that a statistic is sufficient for parameter if the likelihood function can be factorized as: where depends on the data only through and , and depends only on the data and not on .
3. Likelihood Function of Log-Normal
The likelihood function for i.i.d. log-normal observations is: Substituting the log-normal pdf: This can be rewritten as: We aim to express this in the form of the factorization theorem.
4. Factorization and Sufficient Statistic
The likelihood function can be factorized as: From this, we observe that the likelihood depends on the data through:
- (sum of log-transformed values)
- (sum of squared log-transformed values)
Thus, the sufficient statistics are:
Conclusion: Sufficient Statistic
For the log-normal distribution with parameters and , the sufficient statistics, derived using the factorization theorem, are: These capture all the necessary information about and from the data.
Would you like more details on the application of the factorization theorem? Here are 5 related questions for further exploration:
- How does the log-normal distribution relate to the normal distribution?
- What is the interpretation of the sufficient statistic in the context of parameter estimation?
- Can you explain the importance of minimal sufficient statistics?
- How would the Maximum Likelihood Estimators (MLE) be derived for the log-normal distribution?
- 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
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