Sparse least trimmed squares regression for analyzing high-dimensional large data sets
Sparse model estimation is a topic of high importance in modern data analysis due to the
increasing availability of data sets with a large number of variables. Another common …
increasing availability of data sets with a large number of variables. Another common …
Estimation of social exclusion indicators from complex surveys: The R package laeken
Units sampled from nite populations typically come with di fferent inclusion probabilities.
Together with additional preprocessing steps of the raw data, this yields unequal sampling …
Together with additional preprocessing steps of the raw data, this yields unequal sampling …
[图书][B] Sports analytics and data science: winning the game with methods and models
TW Miller - 2015 - books.google.com
This is a complete, practical guide to sports data science and modeling, with examples from
sports industry economics, marketing, management, performance measurement, and …
sports industry economics, marketing, management, performance measurement, and …
Simulation of close-to-reality population data for household surveys with application to EU-SILC
Statistical simulation in survey statistics is usually based on repeatedly drawing samples
from population data. Furthermore, population data may be used in courses on survey …
from population data. Furthermore, population data may be used in courses on survey …
Robust estimation of economic indicators from survey samples based on Pareto tail modelling
Motivated by a practical application, the paper investigates robust estimation of economic
indicators from survey samples based on a semiparametric Pareto tail model. Economic …
indicators from survey samples based on a semiparametric Pareto tail model. Economic …
An object-oriented framework for statistical simulation: The R package simFrame
Simulation studies are widely used by statisticians to gain insight into the quality of
developed methods. Usually some guidelines regarding, eg, simulation designs …
developed methods. Usually some guidelines regarding, eg, simulation designs …
Robust groupwise least angle regression
Many regression problems exhibit a natural grouping among predictor variables. Examples
are groups of dummy variables representing categorical variables, or present and lagged …
are groups of dummy variables representing categorical variables, or present and lagged …
[PDF][PDF] Methoden zur erzeugung synthetischer simulationsgesamtheiten
JP Kolb - 2013 - ubt.opus.hbz-nrw.de
Bei synthetischen Simulationsgesamtheiten handelt es sich um künstliche Daten, die zur
Nachbildung von realen Phänomenen in Simulationen verwendet werden können …
Nachbildung von realen Phänomenen in Simulationen verwendet werden können …
[PDF][PDF] Simulation of synthetic population data for household surveys with application to EU-SILC
Statistical simulation in survey statistics is usually based on repeatedly drawing samples
from population data. Furthermore, population data may be used in courses on survey …
from population data. Furthermore, population data may be used in courses on survey …
Robust variable selection with application to quality of life research
A Alfons, WE Baaske, P Filzmoser, W Mader… - Statistical Methods & …, 2011 - Springer
A large database containing socioeconomic data from 60 communities in Austria and
Germany has been built, stemming from 18,000 citizens' responses to a survey, together …
Germany has been built, stemming from 18,000 citizens' responses to a survey, together …