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Everything about Consistent estimator

Statistical estimator

In statistics, a consistent estimator or asymptotically consistent estimator is an estimator—a rule for computing estimates of a parameter θ0—having the property that as the number of data points used increases indefinitely, the resulting sequence of estimates converges in probability to θ0. This means that the distributions of the estimates become more and more concentrated near the true value of the parameter being estimated, so that the probability of the estimator being arbitrarily close to θ0 converges to one. Wikipedia →

đź§­ Start here: a 6-step reading path

  1. Read the overview: Consistent estimatorA 5-minute orientation
  2. Skim a recent paper: GVINS: Tightly Coupled GNSS–Visual–Inertial Fusion for Smooth and Consistent State EstimationInfluential work since 2022
  3. Pick up a classic book: Estimation of internal consistency and stability reliability using isokinetic segmental curve analysisThe most-reprinted book on the subject
  4. Read the landmark paper: A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for HeteroskedasticityCited 18,846 times
  5. Go sideways: Extremum estimatorA closely related topic
  6. Go sideways: Bernstein–von Mises theoremA closely related topic

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🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

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