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Local regression: History & Products
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced /ˈloʊɛs/…
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Local regression topic overview
The analysis highlights History and Products as prominent areas in the source structure around Local regression.
Related topics
60
Source areas
5
Connected nodes
65
Extracted relationships
80
Concept neighborhoods
24
Bridge connections
65
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
History · 21 topics
Overview · 18 topics
Selection issues: bandwidth, local model, fitting criteria · 10 topics
Model definition · 7 topics
Disadvantages · 4 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Local regression connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Local regression shows recurring relationship patterns in the source. For example, Local regression → Friedman's SuperSmoother, How Opinion, Institute, Local, Local Polynomial Regression Fitting, LOESS, LOESSR, Lowess, Nate Silver, NIST Engineering Statistics Handbook, Quantile, RQuantile LOESS, RR, RThe, Same-Sex Marriage Is Changing, Scatter Plot Smoothing The, Section, Standards, Technology, The Loess Another extracted example is Local regression → Books, Brunsdon, Charlton, Data, Fan, Fotheringham, Geographically Weighted Regression, Gijbels, Its Applications, Katkovnik, Likelihood, Loader, Local Polynomial Modelling, Macaulay, Nonparametric Identification, Russian, Smoothing, The Smoothing, Time Series. Use these groups to spot repeated connection types before inspecting the individual relationships.
Local regression
Top relations
related to External links· 22
Local regression → Friedman's SuperSmoother, How Opinion, Institute, Local, Local Polynomial Regression Fitting, LOESS, LOESSR, Lowess, Nate Silver, NIST Engineering Statistics Handbook, Quantile, RQuantile LOESS, RR, RThe, Same-Sex Marriage Is Changing, Scatter Plot Smoothing The, Section, Standards, Technology, The Loess
related to Further reading· 19
Local regression → Books, Brunsdon, Charlton, Data, Fan, Fotheringham, Geographically Weighted Regression, Gijbels, Its Applications, Katkovnik, Likelihood, Loader, Local Polynomial Modelling, Macaulay, Nonparametric Identification, Russian, Smoothing, The Smoothing, Time Series
related to history· 7
Local regression → Actuarial, An, Following Henderson, Henderson, Local, Robert Henderson, Specifically
related to Localized subsets of data; Bandwidth· 7
Local regression → Careful, Conversely, If, Mathematical, One, The, This
related to Matrix representation of the local regression estimate· 5
Local regression → As, This, Weighted, With, WX
related to Model definition· 5
Local regression → For, In, Local, Stronger, The
related to Choice of fitting criterion· 4
Local regression → As, These, This, Two
related to Selection issues: bandwidth, local model, fitting criteria· 2
Local regression → Implementation, The
is a· 1
Local regression → general term for the fitting procedure
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Local regression relationships Subject–Predicate–Object triples
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 80 structured relationships around Local regression. Examples in this analysis include Local regression → is a → general term for the fitting procedure and cross-validation locally within the smoothing window → instance of → by applying criteria. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Local regression
is a
general term for the fitting procedure
0.90
text
cross-validation locally within the smoothing window
instance of
by applying criteria
0.80
text
cross-validation can be used to compare the fits obtained with different degrees of polynomial.Weight functionAs mentioned above
instance of
methods
0.80
text
the weight function gives the most weight to the data points nearest the point of estimation
instance of
methods
0.80
text
the least weight to the data points that are furthest away
instance of
methods
0.80
text
iteratively reweighted least squares must be used to compute the estimate.Example
instance of
and iterative procedures
0.80
text
LOWESS
instance of
This provides robustness to outliers and high-leverage points without the multiple robustness iterations used in methods
0.80
text
LOESS
instance of
This provides robustness to outliers and high-leverage points without the multiple robustness iterations used in methods
0.80
text
cross-validation can be used to compare the fits obtained with different degrees of polynomial
instance of
methods
0.80
text
Local regression
related to Choice of fitting criterion
As
0.60
section
Local regression
related to Choice of fitting criterion
This
0.60
section
Local regression
related to Choice of fitting criterion
These
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Local regression bring nearby vocabulary together. In this analysis, examples include Regression, Squares and Displaystyle. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Local regression
Regression
Squares
Displaystyle
Polynomial
Model
Likelihood
Degree
Fitting
Function
Data
Estimates
Criterion
local regression
Regression
Squares
Displaystyle
Polynomial
Model
Likelihood
Function
Degree
Fitting
Weighted
Data
Estimates
polynomial regression
Degree
Squares
Hat
Smoothing
Model
Function
Weighted
Data
Displaystyle
Estimates
Criterion
Mu
non-parametric regression
Squares
Function
Weighted
Data
Displaystyle
Estimates
Criterion
Robust
Estimate
Method
Likelihood
Value
"classical" methods
Loess
Regression
Squares
Least
Smoothing
Linear
Bandwidth
Lowess
Used
Fitting
Robust
Method
least squares regression
Squares
Weighted
Loess
Linear
Regression
Function
Methods
Data
Displaystyle
Estimates
Criterion
Points
nonlinear regression
Squares
Function
Weighted
Data
Displaystyle
Estimates
Criterion
Robust
Estimate
Method
Likelihood
Value
probability density function
Displaystyle
Weight
Regression
Left
Local
Mu
Likelihood
Mean
Estimation
Lowess
Estimate
Loess
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Local regression, one of the stronger structural bridges in this analysis connects Local regression with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Local regression
Nodes66
Edges65
Triples80
Avg. degree1.97
Density0.030303
Components1
Source & methodology
TTTA analyzes the structure around Local regression to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
