zbMATH — the first resource for mathematics

Examples
Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

Operators
a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
Fields
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
High leverage point: another source of multicollinearity. (English) Zbl 1129.62395
Summary: Multicollinearity often causes a huge interpretative problem in linear regressions analysis. There is a large body of literature available about the sources of multicollinearity. In our work we find another important and almost inevitable source of multi-collinearity that is the existence of high leverage points in a linear model. Leverage values play very important role in regressions analysis. They often form the basis of regression diagnostics as measures of influential observations in the explanatory variables. It is generally believed that high leverage points are responsible for causing masking of outliers in linear regression. But we observe that high leverage points may also be responsible for causing multicollinearity. We present few examples and figures, which draw our attention to this problem. Then we investigate the nature and extend of multicollinearity caused by the high leverage points through Monte Carlo simulation experiments.
MSC:
62J05Linear regression
62J20Regression diagnostics