Statistical Methodology for Large Astronomical Surveys

De-Trending Time Series for Astronomical Variability Surveys

We present a de-trending algorithm for the removal of trends in time series. Trends in time series could be caused by various systematic and random noise sources such as cloud passages, changes of airmass, telescope vibration or CCD noise. Those trends undermine the intrinsic signals of stars and should be removed. We determine the trends from subsets of stars that are highly correlated among t...

Algorithms for Large-Scale Astronomical Problems

Modern astronomical datasets are getting larger and larger, which already include billions of celestial objects and take up terabytes of disk space. Meanwhile, many astronomical applications do not scale well to such large amount of data, which raises the following question: How can we use modern computer science techniques to help astronomers better analyze large datasets? To answer this quest...

Statistical Matching in ‘Labour Force’ and ‘Time Use’ Surveys

Abstract. In the National Statistical Systems‎, ‎some parts of the official statistics required for development planning and evidence base management are collected through sampling survey‎. ‎Due to the high cost of sampling process‎, ‎as well as nonresponses‎, ‎it may not be possible to collect all of the expected variables in a survey‎.‎In order to obtain a comprehensive and complete source o...

Statistical Methodology for Deformations

Nonparametric density estimation and clustering in astronomical sky surveys

We present a nonparametric method for galaxy clustering in astronomical sky surveys. We show that the cosmological definition of clusters of galaxies is equivalent to density contour clusters (Hartigan , 1975) Sc = {f > c} where f is a probability density function. The plug-in estimator Ŝc = {f̂ > c} is used to estimate Sc where f̂ is the multivariate kernel density estimator. To choose the optim...