Kernel Regression Model with Correlated Errors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F14%3A00074716" target="_blank" >RIV/00216224:14310/14:00074716 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Kernel Regression Model with Correlated Errors
Original language description
Kernel regression is one of the commonly used nonparametric methods for an estimation of a regression function. Nevertheless, there is a problem of choosing the value of the smoothing parameter, the bandwidth. In the case of independent observations theliterature on the bandwidth selection is quite extensive. However, these standard methods, like cross-validation, perform badly when the errors are correlated. There are several possibilities how to overcome this. We will present and compare the partitioned cross-validation method and the plug-in method.
Czech name
—
Czech description
—
Classification
Type
C - Chapter in a specialist book
CEP classification
BA - General mathematics
OECD FORD branch
—
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Theoretical and Applied Issues in Statistics and Demography
ISBN
9786188125773
Number of pages of the result
12
Pages from-to
3-14
Number of pages of the book
349
Publisher name
International Society for the Advancement of Science and Technology (ISAST)
Place of publication
Athens
UT code for WoS chapter
—