Cramér-Rao Lower Bound: Introduction

Key concept: Cramér-Rao bound is the lower bound on variance of unbiased estimators that estimate deterministic parameters.

Introduction

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 unbiased and that give minimum variance (compared to the true parameter value). This is given by the following two equations

For a MVUE, it is easier to verify the first criteria (unbiased-ness) using the first equation, but verifying the second criteria (minimum variance) is tricky. We can only calculate the variance of the estimator, but how can we make sure that it is “the minimum”? How can we make sure that a designed estimator gives the minimum variance? There may exist other numerous unbiased estimators (which we may not know) that may give minimum variance. Other words, how do we make sure that our estimate is the best MVUE in the world? Cramér-Rao Lower Bound (CRLB) may come to our rescue.

Cramér-Rao Lower Bound (CRLB):

Harald Cramér and Radhakrishna Rao derived a way to express the lower bound on the variance of unbiased estimators that estimate deterministic parameters. This lower bound is called as the Cramér-Rao Lower Bound (CRLB).

If is an unbiased estimate of a deterministic parameter , then the relationship between the variance of the estimates ( ) and CRLB can be expressed as

CRLB tell us the best minimum variance that we can expect to get from an unbiased estimator.

Applications of CRLB include :

1) Making judgment on proposed estimators. Estimators whose variance is not close to CRLB are considered inferior.
2) To do feasibility studies as to whether a particular estimator/system can meet given specifications. It is also used to rule out impossible estimators – No estimator can beat CRLB (example: Figure 1).
3) Benchmark for comparing unbiased estimators.
4) It may sometimes provide MVUE. If an unbiased estimator achieved CRLB, it means that it is a MVUE.

Cramer Rao Lower Bound for asymptotically efficient estimator
Figure 1: CRLB and the efficient estimator for phase estimation

Feasibility Studies :

Derivation of CRLB for a particular given scenario or proposed algorithm of estimation is often found in research texts. The derived theoretical CRLB for a system/algorithm is compared with actual variance of the implemented system and conclusions are drawn. For example, in the paper titled “A Novel Frequency Synchronization Algorithm and its Cramer Rao Bound in Practical UWB Environment for MB-OFDM Systems”[1] – a frequency offset estimation algorithm was proposed for estimating frequency offsets in multi-band orthogonal frequency division multiplexing (MB-OFDM) systems. The performance of the algorithm was studied by BER analysis (Eb/N0 Vs BER curves). Additionally,the estimator performance is further validated by comparing the simulated estimator variance with the derived theoretical CRLB for four UWB channel models.

Rate this article: Note: There is a rating embedded within this post, please visit this post to rate it.

Reference

[1] Debrati et al,“A Novel Frequency Synchronization Algorithm and its Cramer Rao Bound in Practical UWB Environment for MB-OFDM Systems”, RADIOENGINEERING, VOL. 18, NO. 1, APRIL 2009.↗

Similar topics

[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)

Books by the author


Wireless Communication Systems in Matlab
Second Edition(PDF)

(180 votes, average: 3.62 out of 5)

Checkout Added to cart

Digital Modulations using Python
(PDF ebook)

(134 votes, average: 3.56 out of 5)

Checkout Added to cart

Digital Modulations using Matlab
(PDF ebook)

(136 votes, average: 3.63 out of 5)

Checkout Added to cart
Hand-picked Best books on Communication Engineering
Best books on Signal Processing

Published by

Mathuranathan

Mathuranathan Viswanathan, is an author @ gaussianwaves.com that has garnered worldwide readership. He is a masters in communication engineering and has 12 years of technical expertise in channel modeling and has worked in various technologies ranging from read channel, OFDM, MIMO, 3GPP PHY layer, Data Science & Machine learning.

2 thoughts on “Cramér-Rao Lower Bound: Introduction”

  1. Hi, how can I see the latex lines properly? I have tried different browsers (IE, Chrome and Safari) with no luck. What I see is for example ““. Thanks!

Post your valuable comments !!!Cancel reply