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: Cramér–Rao lower bound | Statement: [Cramér–Rao bound, alsoKnownAs, Cramér–Rao lower bound]

Target entity: Cramér–Rao lower bound
Context triple: [Cramér–Rao bound, alsoKnownAs, Cramér–Rao lower bound]

• A. Cramér–Rao bound chosen
  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.
• B. Fisher information
  Fisher information is a fundamental concept in statistics that quantifies how much information an observable random variable carries about an unknown parameter, playing a key role in estimation theory and the Cramér–Rao bound.
• C. Chernoff information
  Chernoff information is a measure in information theory and statistics that quantifies the exponential rate at which the error probability decays when optimally distinguishing between two probability distributions.
• D. Neyman–Pearson theory of hypothesis testing
  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.
• 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.