Sökning: "linear rank statistics"
Visar resultat 1 - 5 av 11 avhandlingar innehållade orden linear rank statistics.
1. Rank Estimation in Elliptical Models : Estimation of Structured Rank Covariance Matrices and Asymptotics for Heteroscedastic Linear Regression
Sammanfattning : This thesis deals with univariate and multivariate rank methods in making statistical inference. It is assumed that the underlying distributions belong to the class of elliptical distributions. LÄS MER
2. Contributions to generalized Wilcoxon rank tests
Sammanfattning : In unbalanced small sample size problems with right censorings, the variance estmators of linear rank statistics, may be biased. This is the case with the commonly applied variance estimators for the generalized Wilcoxon rank test statistics of Gehan and Prentice. LÄS MER
3. Aspects of analysis of small-sample right censored data using generalized Wilcoxon rank tests
Sammanfattning : The estimated bias and variance of commonly applied and jackknife variance estimators and observed significance level and power of standardised generalized Wilcoxon linear rank sum test statistics and tests, respectively, of Gehan and Prentice are compared in a Monte Carlo simulation study. The variance estimators are the permutational-, the conditional permutational- and the jackknife variance estimators of the test statistic of Gehan, and the asymptotic- and the jackknife variance estimators of the test statistic of Prentice. LÄS MER
4. Toward Sequential Data Assimilation for NWP Models Using Kalman Filter Tools
Sammanfattning : The aim of the meteorological data assimilation is to provide an initial field for Numerical Weather Prediction (NWP) and to sequentially update the knowledge about it using available observations. Kalman filtering is a robust technique for the sequential estimation of the unobservable model state based on the linear regression concept. LÄS MER
5. Data driven modeling in the presence of time series structure: : Improved bounds and effective algorithms
Sammanfattning : This thesis consists of five appended papers devoted to modeling tasks where the desired models are learned from data sets with an underlying time series structure. We develop a statistical methodology for providing efficient estimators and analyzing their non-asymptotic behavior. LÄS MER