Measurement, Regression, and Calibration

By Raymond J. Carroll. 1994

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Citations in the corpus

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2021, background): “Calibration [33] refers to the ability of a model to make good probabilistic predictions.”
Bayesian Wavelet Regression on Curves With Application to a Spectroscopic Calibration Problem (2001, background): “(More details of these notations, and a corresponding form for the matrix-variate T , can be found in Brown 1993, App.”
Simultaneous Variable Selection (2005, methodology): “The idea is a natural extension of the LASSO technique proposed by Tibshirani (1996) and is based on the ( joint) residual sum of squares while constraining the parameter estimates to lie within a suitable polyhedral region.”
On Performance of Shrinkage Methods – A Monte Carlo Study (2015, methodology): “Traditional statistical estimation procedures such as Ordinary Least Squares (OLS) tend to perform poorly, have high prediction variance, and may be difficult to interpret [1] i.”
Applied Linear Regression (2004, background): “This problem is called inverse regression, and is discussed by Brown (1993).”
Inference with normal-gamma prior distributions in regression problems (2010, methodology): “As we shall show in Section 2, this prior induces adaptive matrix shrinkage (Chamberlain and Leamer 1976) where the norm |E[β|y]| is a decreasing function of |β̂| and larger |β̂| are shrunk less than smaller |β̂|. However, for fixed design X, very large |β̂| shrinkage asymptotes to some non-zero val”
Comparisons and validation of statistical clustering techniques for microarray gene expression data (2003, background): “For a more detailed account on partial least squares the reader may consult Stone and Brooks (1990) and Brown (1993).”
On the use of cross-validation to assess performance in multivariate prediction (2000, methodology): “10) on page 45 of Brown (1993) with its training set counterpart as deduced from the third equation on page 41 of the same reference.”
The Superiority of Simple Alternatives to Regression for Social Science Predictions (2004, methodology): “We employed a data-dependent estimate of k given by Brown (1993) and motivated in the Bayesian manner described above, which in our case can be reduced to:”
On the LASSO and its Dual (2000, background): “This can be a rather severe practical limitation, since problems in which the number of variables is of the same or larger order than the number of observations occur frequently in areas such as chemometrics, where partial least squares (Brown, 1993; Haagen et al., 1993) is often employed.”