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Local Variance Estimation for Uncensored and Censored Observations

LOCAL VARIANCE ESTIMATION FOR UNCNSRD & CNSRD OBSRVTNS

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

Paola Gloria Ferrario develops and investigates several methods of nonparametric local variance estimation. The first two methods use regression estimations (plug-in), achieving least squares estimates as well as local averaging estimates (partitioning or kernel type). Furthermore, the author uses a partitioning method for the estimation of the local variance based on first and second nearest neighbors (instead of regression estimation). Approaching specific problems of application fields, all the results are extended and generalised to the case where only censored observations are available. Further, simulations have been executed comparing the performance of two different estimators (R-Code available!). As a possible application of the given theory the author proposes a survival analysis of patients who are treated for a specific illness.

POST SECONDARY FOUR YEAR

COLFR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Other Frontmatter.
Acknowledgements.
Deutsche Zusammenfassung.
Contents.
List of Figures.
List of Abbreviations.
1: Introduction.
2: Least Squares Estimation via Plug-In.
3: Local Averaging Estimation via Plug-In.
4: Partitioning Estimation via Nearest Neighbors.
5: Local Variance Estimation for Censored Observations.
6: Simulations.
References.

Local Variance Estimation for Uncensored and Censored Observations.css-1p8rvzy{position:absolute;padding-left:8px;}

Local Variance Estimation for Uncensored and Censored Observations