← Back to catalogue
Research draft

estimator

vr.tr.estimator · XCT.QLT

Let an agent define estimators and their properties, relay major kinds and evaluation criteria from statistical references, explain their use in data analysis and machine learning, and distinguish the statistical sense from the occupational sense.

Thing Registry Cross-cutting context

Research draft, second pass

A second pass drafted this model: the structure a model of this thing needs, and what is known about it in the world. The line under this one says how the second half was obtained - researched against sources, or recalled without web access, in which case nothing here was read anywhere and every claim is a lead to verify. Unreviewed either way.

written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify

Researched by: Claude

Purpose and description

Let an agent define estimators and their properties, relay major kinds and evaluation criteria from statistical references, explain their use in data analysis and machine learning, and distinguish the statistical sense from the occupational sense.

In statistics, a rule or function that computes an estimate of an unknown population parameter from sample data, such as the sample mean as an estimator of the population mean, maximum likelihood estimators, unbiased estimators, kernel density estimators and robust estimators such as RANSAC used in computer vision; estimators are evaluated by properties such as bias, variance, consistency and efficiency, and the term also names a person who estimates costs in construction and insurance.

What it is for: Estimating unknown quantities from data.

It can be define and explain properties; relay major kinds; explain use in analysis; distinguish senses.

Distinguishing features

Function of the sample

Bias and variance

Consistency and efficiency

Many construction principles

What it looks like

Not a visible object; a formula or algorithm applied to data.

How it is recognised

Rule computing an estimate from a sample

Mean, maximum likelihood, unbiased, kernel, robust estimators

An estimate is the value produced; a cost estimator is an occupation

Related models

is a kind of - in registry terms

statistic

is a kind of - in registry terms

function

estimates - of a population or model

parameter

is evaluated by - among other criteria

mean squared error

In practice

Families and kinds

point estimators such as the sample mean and variance

maximum likelihood and method of moments estimators

unbiased and minimum variance estimators

Bayesian estimators

nonparametric estimators such as kernel density estimation

robust estimators such as RANSAC and M-estimators

estimators in machine learning such as tree-structured Parzen estimators

Standards and regulation

Statistical methodology standards in official statistics

No regulation of the concept

Failure modes and hazards

Biased or inconsistent estimators

Misapplied assumptions

Confusing estimator and estimate

Confusing with the occupation

Also called

multivariate kernel density estimationRANSACunbiased estimatormaximum likelihood estimatorParzen-Tree Estimatorregular estimatorefficient estimatorTrimmed estimatorS-estimatorkernel density estimationadaptive estimatorM-estimatorBrain connectivity estimatorsExtremum estimatorFirst-difference estimatorHodges' estimatorInvariant estimatorL-estimatorshrinkage estimatorBayes estimator

Where this came from

wikidata · CC0 1.0

Source repository Registry entry point

Drafted structure

Bundle to layer to finding to question, as the second pass will find it: 4 bundles · 8 layers · 8 findings · 16 questions.

Define Definition and properties.

Statistics.

Definition

Definition.

Definition

Definition.

  1. What is an estimator, and how does it differ from an estimate and a parameter? definition
  2. Is the question about statistics or the cost estimating occupation? boundary

Properties

Properties.

Properties

Properties.

  1. What are bias, variance, consistency, efficiency and sufficiency? definition
  2. Which entry fits the specific property? action
Kinds Kinds of estimator.

Statistics.

Classical

Classical estimators.

Classical

Classical.

  1. How do maximum likelihood, method of moments and least squares estimators work? provenance
  2. Which references are standard? provenance

Modern

Nonparametric and robust.

Modern

Modern.

  1. How do kernel density estimators, RANSAC and other robust and nonparametric methods work? provenance
  2. Which entry fits the specific method? action
Apply Applications.

Practice.

Analysis

Data analysis.

Analysis

Analysis.

  1. How are estimators chosen and evaluated in practice, including the bias-variance trade-off? action
  2. Which entry fits statistical inference? action

Learning

Machine learning.

Learning

Learning.

  1. How do estimators appear in machine learning, including hyperparameter methods such as tree-structured Parzen estimators? provenance
  2. Which sources are cited? provenance
Context Theory and history.

Context.

Theory

Theory.

Theory

Theory.

  1. What do the Cramer-Rao bound and decision theory say about optimal estimators? provenance
  2. Which entry fits estimation theory? action

History

History.

History

History.

  1. How did estimation theory develop from Gauss and Laplace to Fisher and beyond? provenance
  2. Which entry fits the history of statistics? action

What the second pass must settle

  • Should maximum likelihood estimation be a separate primary entry?
  • How should statistical references be linked?
  • The registry entry has merged aliases naming specific methods; should they be split off?
Draft as JSON