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White noise testing using wavelets Early View

  • Journal: Stat
  • Authors: Guy P. Nason, Delyan Savchev
  • Published Date: Dec 04, 2014

Testing whether a time series is consistent with white noise is an important task within time series analysis and for model fitting and criticism via residual diagnostics. We introduce...

Correcting for non‐ignorable missingness in smoking trends Early View

  • Journal: Stat
  • Authors: Juho Kopra, Tommi Härkänen, Hanna Tolonen, Juha Karvanen
  • Published Date: Jan 29, 2015

Data missing not at random (MNAR) are a major challenge in survey sampling. We propose an approach based on registry data to deal with non‐ignorable missingness in health examination...

Ensemble simulations of inertial confinement fusion implosions Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Ryan Nora, Jayson Luc Peterson, Brian Keith Spears, John Everett Field, Scott Brandon
  • Published Date: May 24, 2017

The achievement of inertial confinement fusion ignition on the National Ignition Facility relies on the collection and interpretation of a limited (and expensive) set of experimental data....

Selecting the selector: Comparison of update rules for discrete global optimization Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: James Theiler, Beate G. Zimmer
  • Published Date: May 24, 2017

We compare some well‐known Bayesian global optimization methods in four distinct regimes, corresponding to high and low levels of measurement noise and to high and low levels of “quenched...

A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Qiwei Li, Michele Guindani, Brian J. Reich, Howard D. Bondell, Marina Vannucci
  • Published Date: Jun 08, 2017

In this paper, we consider the problem of modeling a matrix of count data, where multiple features are observed as counts over a number of samples. Due to the nature of the data generating...

A statistical approach to combining multisource information in one‐class classifiers Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Katherine M. Simonson, R. Derek West, Ross L. Hansen, Thomas E. LaBruyere, Mark H. Van Benthem
  • Published Date: Jun 08, 2017

A new method is introduced for combining information from multiple sources to support one‐class classification. The contributing sources may represent measurements taken by different...

Bayesian kernel machine models for testing genetic pathway effects in prostate cancer prognosis Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Chang Xu, Sounak Chakraborty
  • Published Date: Jun 09, 2017

In this paper we propose a Bayesian semiparametric regression model to estimate and test the effect of a genetic pathway on prostate‐specific antigen (PSA) measurements for patients with...

Random forest missing data algorithms Early View

  • Journal: Statistical Analysis and Data Mining: The ASA Data Science Journal
  • Authors: Fei Tang, Hemant Ishwaran
  • Published Date: Jun 13, 2017

Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data,...

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