CRLB for scalar parameter estimation was discussed in previous posts. The same concept is extended to vector parameter estimation. Consider a set of deterministic parameters \(\mathbb{\theta}=[ \theta_1, \theta_2, …, \theta_p]^{T}…

# Tag: CRLB

The variance of an estimate is always greater than or equal to Cramer Rao Lower Bound of the estimate. The CRLB is in turn given by inverse of Fisher Information.The…

Consider the DSB carrier frequency estimation problem given in the introductory chapter to estimation theory. A message is sent across a channel modulated by a sinusoidal carrier with carrier frequency…

It has been reiterated that not all estimators are efficient. Even not all the MVUE are efficient. Then how do we quantify whether the estimator designed by us is efficient…

It was mentioned in one of the earlier articles that CRLB may provide a way to find a MVUE (Minimum Variance Unbiased Estimators). Theorem: There exists an unbiased estimator that…

Consider a set of observed data samples \( X={X_1,X_2,X_3,\cdots X_N} \) and \( \theta \) is the scalar parameter that is to be estimated from the observed samples. The accuracy…

The criteria for existence of having an Minimum Variance Unbiased Estimator (MVUE) was discussed in a previous article. To have an MVUE, it is necessary to have estimates that are…

As discussed in the introduction to estimation theory, the goal of an estimation algorithm is to give an estimate of random variable(s) that is unbiased and has minimum variance. This…

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