Abstract: Peelle’s Pertinent Puzzle (PPP) was described in 1987 in the context of estimating fundamental parameters that arise in nuclear interaction experiments. In PPP, generalized least squares (GLS) parameter estimates fell outside the range of the data, which has raised concerns that GLS is somehow flawed and has led to suggested alternatives to GLS estimators. However, there have been no corresponding performance comparisons among methods, and one suggested approach involving simulated data realizations is statistically incomplete. Here we provide performance comparisons among estimators, introduce approximate Bayesian computation (ABC) using density estimation applied to simulated data realizations to produce an alternative to the incomplete approach, complete the incompletely specified approach, and show that estimation error in the assumed covariance matrix cannot always be ignored.
Keywords: approximate Bayesian computation using density estimation; mean squared error; Peelle’s puzzle
Export to BibTeX
MDPI and ACS Style
Burr, T.; Graves, T.; Hengartner, N.; Kawano, T.; Pan, F.; Talou, P. Alternatives to the Least Squares Solution to Peelle’s Pertinent Puzzle. Algorithms 2011, 4, 115-130.
Burr T, Graves T, Hengartner N, Kawano T, Pan F, Talou P. Alternatives to the Least Squares Solution to Peelle’s Pertinent Puzzle. Algorithms. 2011; 4(2):115-130.
Burr, Tom; Graves, Todd; Hengartner, Nicolas; Kawano, Toshihiko; Pan, Feng; Talou, Patrick. 2011. "Alternatives to the Least Squares Solution to Peelle’s Pertinent Puzzle." Algorithms 4, no. 2: 115-130.