Results 31 to 40 of about 15,062,519 (328)
A survey of data partitioning and sampling methods to support big data analysis
Big Data Mining and Analytics, 2020 Computer clusters with the shared-nothing architecture are the major computing platforms for big data processing and analysis. In cluster computing, data partitioning and sampling are two fundamental strategies to speed up the computation of big data and
M.S. Mahmud +4 more
semanticscholar +1 more source
Subsampling MCMC - An introduction for the survey statistician [PDF]
, 2018 The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work.
Dang, Khue-Dung +4 more
core +1 more source
Quality procedures for survey transitions - experiments, time series and discontinuities
Survey Research Methods, 2008 To maintain uninterrupted time series, surveys conducted by national statistical institutes are often kept unchanged as long as possible. When a change is proposed to improve the methods, it may affect the continuity of these series. It is important to
Jan van den Brakel +2 more
doaj +1 more source
Internet mobility survey sampling biases in measuring frequency of use of transport modes [PDF]
, 2012 We develop a quantitative analysis of the biases that arise when measuring trip frequencies for a general population through an online survey instrument.
Diana, Marco
core +1 more source
Entropy Balancing is Doubly Robust
Journal of Causal Inference, 2016 Covariate balance is a conventional key diagnostic for methods estimating causal effects from observational studies. Recently, there is an emerging interest in directly incorporating covariate balance in the estimation.
Zhao Qingyuan, Percival Daniel
doaj +1 more source
Propensity Score Weighting for Causal Inference with Clustered Data
Journal of Causal Inference, 2018 Propensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering, which make propensity score modeling and estimation challenging.
Yang Shu
doaj +1 more source
Interactive Linear Models in Survey Sampling [PDF]
Journal of Statistical Theory and Applications (JSTA), 2014 Considered is a linear ‘interactive’ model in the context of survey sampling. This situation arises when investigator and/or supervisor interventions are contemplated in the responses.
Bikas K. Sinha, Pulakes Maiti
doaj +1 more source
Resampling methods in survey sampling
Lietuvos Matematikos Rinkinys, 2000 Aim of this paper is to estimate variances of the estimates of the population totals using resampling methods and to compare them with the known variances of the estimates.
Danutė Krapavickaitė +1 more
doaj +3 more sources
The Savvy Survey #3: Successful Sampling
EDIS, 2014 As part of the Savvy Survey series, this publication provides Extension faculty with an overview of topics to consider when thinking about who should be surveyed.
Jessica L. Gouldthorpe, Glenn D. Israel
doaj +5 more sources
Minimax strategies in survey sampling [PDF]
, 1999 The risk of a sampling strategy is a function on the parameter space, which is the set of all vectors composed of possible values of the variable of interest. It seems natural to ask for a minimax strategy, minimizing the maximal risk.
Bickel +13 more
core +1 more source