Minimum-variance unbiased estimator (MVUE)

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 criteria is reproduced here for reference

In the above equations f₀ is the transmitted carrier frequency and is the estimated frequency based on a set of observed data (See previous article).

Existence of minimum-variance unbiased estimator (MVUE):

The estimator described above is called minimum-variance unbiased estimator (MVUE) since, the estimates are unbiased as well as they have minimum variance. Sometimes there may not exist any MVUE for a given scenario or set of data. This can happen in two ways
1) No existence of unbiased estimators
2) Even if we have unbiased estimator, none of them gives uniform minimum variance.

Consider that we have three unbiased estimators g₁, g₂ and g₃ that gives estimates of a deterministic parameter θ. Let the unbiased estimates be , and respectively.

Figure 1 illustrates two scenarios for the existence of an MVUE among the three estimators. In Figure 1a, the third estimator gives uniform minimum variance compared to other two estimators. In Figure 1b, none of the estimator gives minimum variance that is uniform across the entire range of θ.

Methods to find MVU Estimator:

1) Determine Cramer-Rao Lower Bound (CRLB) and check if some estimator satisfies it. If an estimator exists whose variance equals the CRLB for each value of θ, then it must be the MVU estimator. It may happen that no estimator exists that achieve CRLB.

2) Use Rao-Blackwell-Lechman-Scheffe (RBLS) Theorem: Find a sufficient statistic and find a function of the sufficient statistic. This function gives the MVUE. This approach is rarely used in practice.

3) Restrict the solution to find linear estimators that are unbiased. This gives Minimum Variance Linear Unbiased Estimator (MVLUE). This method gives MVLUE only if the problem is truly linear.

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For further study

[1] Notes on Cramer-Rao Lower Bound (CRLB).↗
[2] Notes on Rao-Blackwell-Lechman-Scheffe (RBLS) Theorem.↗

See Also

[1]	An Introduction to Estimation Theory
[2]	Bias of an Estimator
[3]	Minimum Variance Unbiased Estimators (MVUE)
[4]	Maximum Likelihood Estimation
[5]	Maximum Likelihood Decoding
[6]	Probability and Random Process
[7]	Likelihood Function and Maximum Likelihood Estimation (MLE)
[8]	Score, Fisher Information and Estimator Sensitivity
[9]	Introduction to Cramer Rao Lower Bound (CRLB)
[10]	Cramer Rao Lower Bound for Scalar Parameter Estimation
[11]	Applying Cramer Rao Lower Bound (CRLB) to find a Minimum Variance Unbiased Estimator (MVUE)
[12]	Efficient Estimators and CRLB
[13]	Cramer Rao Lower Bound for Phase Estimation
[14]	Normalized CRLB - an alternate form of CRLB and its relation to estimator sensitivity
[15]	Cramer Rao Lower Bound (CRLB) for Vector Parameter Estimation
[16]	The Mean Square Error – Why do we use it for estimation problems
[17]	How to estimate unknown parameters using Ordinary Least Squares (OLS)
[18]	Essential Preliminary Matrix Algebra for Signal Processing
[19]	Why Cholesky Decomposition ? A sample case:
[20]	Tests for Positive Definiteness of a Matrix
[21]	Solving a Triangular Matrix using Forward & Backward Substitution
[22]	Cholesky Factorization - Matlab and Python
[23]	LTI system models for random signals – AR, MA and ARMA models
[24]	Comparing AR and ARMA model - minimization of squared error
[25]	Yule Walker Estimation
[26]	AutoCorrelation (Correlogram) and persistence – Time series analysis
[27]	Linear Models - Least Squares Estimator (LSE)
[28]	Best Linear Unbiased Estimator (BLUE)

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