Parameters, Statistics, Estimands, and Estimators
Install and import#
npm install fintech-algorithmsimport { parametersStatisticsEstimandsAndEstimators } from "fintech-algorithms/foundations/sampling-estimation-and-statistical-inference/parameters-statistics-estimands-and-estimators";Signature#
parametersStatisticsEstimandsAndEstimators(input)Places the known population parameter from input.populationMean alongside the statistic computed from input.sample, and names which side is the estimand and which is the estimator.
Parameters#
| Name | Type | Notes |
|---|---|---|
input | D00Input | One record. sample is the observed data and populationMean is the parameter it is meant to estimate. estimates and alpha are validated for the whole family before dispatch, so they must be present and valid here too.sample: non-empty list of finite numbers, at least two of them · estimates: non-empty list of finite numbers · alpha: strictly between 0 and 1 |
Returns#
D00Output
An object with parameter (echoes populationMean), statistic (the sample mean), and the fixed labels estimand and estimator.
Errors#
- When sample or estimates is missing, empty, or contains a non-finite number — both are parsed for every topic in the family, whether or not the topic uses them — throws RangeError
- When sample holds fewer than two observations — throws RangeError
- When alpha is not strictly between zero and one — throws RangeError
Complexity: time O(n + m),
space O(n + m).
Worked example#
verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.
Input#
{
"sample": [2, 3, 4, 3, 4],
"estimates": [3, 3.1, 3.2, 3.3, 3.4],
"populationMean": 3.2,
"alpha": 0.05,
"mseBenchmark": 0.1,
"nullMean": 3,
"alternativeMean": 3.5,
"practicalThreshold": 0.1,
"comparisons": 5
}Call#
parametersStatisticsEstimandsAndEstimators(input)Returns#
object with 2 fields: parameter, statistic
{
"parameter": 3.2,
"statistic": 3.2
}Diagrams#
How it works#
This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.
References#
- Product and Process Comparisons — NIST/SEMATECH e-Handbook
- Confidence Intervals — NIST/SEMATECH e-Handbook
- Historical-example decision