Local Variance Estimation for Uncensored and Censored Observations 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. Contents · Least Squares Estimation of the Local Variance via Plug In · Local Averaging Estimation of the Local Variance via Plug In · Partitioning Estimation of the Local Variance via Nearest Neighbors · Estimation of the Local Variance under Censored Observations Target Groups · Researchers and graduate students in the fields of mathematics and statistics · Practitioners in the fields of medicine reliability finance and insurance Author Paola Gloria Ferrario received her doctorate degree (doctor rerum naturalium) from the University of Stuttgart Germany in 2012 after having studied Mathematical Engineering at the Polytechnic of Milano Italy. She taught mathematics to students of economics at University of Hohenheim and now works as a researcher at the University of Lübeck Germany. Least Squares Estimation of the Local Variance via Plug In. Local Averaging Estimation of the Local Variance via Plug In Partitioning Estimation of the Local Variance via Nearest Neighbors Estimation of the Local Variance under Censored Observations.