Triple

Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).

Phrase: Neyman–Pearson lemma | Statement: [Neyman–Pearson theory of hypothesis testing, hasPart, Neyman–Pearson lemma]

Target entity: Neyman–Pearson lemma
Context triple: [Neyman–Pearson theory of hypothesis testing, hasPart, Neyman–Pearson lemma]

• A. Neyman–Pearson theory of hypothesis testing chosen
  The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
• B. Statistical Decision Functions
  Statistical Decision Functions is a foundational work in decision theory and statistics that systematically develops the theory of optimal decision-making under uncertainty.
• C. Cramér–Rao bound
  The Cramér–Rao bound is a fundamental result in statistical estimation theory that gives a lower limit on the variance of any unbiased estimator of a parameter, characterizing the best possible precision achievable.
• D. Wald test
  The Wald test is a statistical hypothesis test used to assess the significance of individual coefficients or parameters in a model, particularly in regression and maximum likelihood estimation.
• E. Gauss–Markov theorem
  The Gauss–Markov theorem is a fundamental result in statistics stating that, under certain conditions, the ordinary least squares estimator is the best linear unbiased estimator (BLUE) of the coefficients in a linear regression model.
• F. None of above.
• G. Unsure - the case is ambiguous/there is not enough information to decide.