Search results for "Large dataset"
showing 4 items of 4 documents
Synchrotron Radiation-Based Micro-XANES and Micro-XRF Study of Unsuccessfully Produced Egyptian Blue from the Late Hellenistic Production Site of Kos…
2021
International audience; This paper examines the production technology of Egyptian blue, an ancient artificial pigment, through the investigation of an unsuccessfully produced pellet derived from the Hellenistic production site of Kos (Dodecanese, Greece). This heterogeneous material was investigated by a combination of laboratory and synchrotron radiation-based (SR) techniques: scanning electron microscopy coupled with energy-dispersive Xray spectrometry, micro-Raman spectroscopy, high-resolution SR micro-X-ray fluorescence spectroscopy, and SR micro-X-ray absorption near-edge structure spectroscopy (XANES), at the ID21 beamline of the European Synchrotron Radiation Facility. Principal comp…
A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data
2008
The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest the use of the principal component methodology of Stock and Watson [Stock, J.H., Watson, M.W., 2002. Macroeconomic forecasting using diffusion indices. Journal of Business and Economic Statistics, 20, 147–162] for the stochastic volatility factor model discussed by Harvey, Ruiz, and Shephard [Harvey, A.C., Ruiz, E., Shephard, N., 1994. Multivariate Stochastic Variance Models. Review of Economic Studies, 61, 247–264]. We provide theoretical and Monte Carlo results on this method and apply it to S&P data.
speedglm: Fitting Linear and Generalized Linear Models to large data sets.
2009
This is an R packge to fit (generalized) linear models to large data sets. For data loaded in R memory the fitting is usually fast, especially if R is linked against an optimized BLAS. For data sets of size greater of R memory, the fitting is made by an updating algorithm
Selecting significant respondents from large audience datasets: The case of the World Hobbit Project
2016
International projects, online questionnaires, or data mining techniques now allow audience researchers to gather very large and complex datasets. But whilst data collection capacity is hugely growing, qualitative analysis, conversely, becomes increasingly difficult to conduct. In this paper, I suggest a strategy that might allow the researcher to manage this complexity. The World Hobbit Project dataset (36,109 cases), including answers to both closed and open-ended questions, was used for this purpose. The strategy proposed here is based on between-methods sequential triangulation, and tries to combine statistical techniques (k-means clustering) with textual analysis. K-means clustering pe…