By Chi-Lun Cheng, John W. Van Ness (auth.), Prof. Dr. Robert Galata, Prof. Dr. Helmut Küchenhoff (eds.)
Hans SchneeweiB is- one of many best-known German econometricians and statisticians. He was once born in Glatz, Silesia, on March thirteen, 1933. Hans SchneeweiB studied arithmetic and physics and acquired his Ph. D. measure from the Johann-Wolfgang-Goethe college, Frankfurt, in 1960. He was once member of the educational employees of the college of legislations and Economics of the Saar college among 1959 and 1965. Following his Habilitation in 1964, he used to be appointed to the chair of information and Econometrics on the similar college. As a traveling professor he labored on the Institute for Aca demic stories in Vienna in 1967 and on the division of data of the collage of Waterloo, Canada, in 1970/71. He has been an entire professor of Econometrics and information on the Ludwig-Maximilians-University in Mu nich due to the fact 1973. His large learn actions in a foreign country incorporated vital initiatives in Waterloo, Vienna, Dundee (Scotland), Sidney, China, Kiev. in the course of the greater than forty years of his educational paintings he has released remarkable, unique articles on econometrics and records. to offer an ex considerable, it really is his learn on choice thought which has marked advancements during this box. His booklet Entscheidungskriterien bei Risiko, released in 1967, is a wonderful beginning for someone searching for an advent to the complicated concerns involved.
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Extra info for Econometrics in Theory and Practice: Festschrift for Hans Schneeweiß
For all simulations the average squared error (ASE) was calculated for each type of spline to facilitate comparisons. The spline coefficients were calculated using the ridge regression method of Eilers & Marx (1996) using a quadratic order spline with 10 equally spaced knot points. Logistic Regression Comparison ~ True CUlVe StnJetural. 0 X values Fig. 1. Comparison of Gibbs spline to naive spline. First, we compare the structural spline to the naive spline. For this example, there were 1000 data points, (X), which were generated from an Uniform(O,l) random variable.
19, 1-21. 14. C. and Spiegelman, D. (1989). Correction of logistic regression relative risk estimates and confidence intervals for systematic withinperson measurement error. Statistics in Medicine. 8, 1051-1069. f:;: 44 Markus Thamerus 15. W. (1993). Likelihood analysis for probit regression with measurement error. Biometrika. 80, 899-904. 16. W. G. (1996). Likelihood analysis for errors-in-variable regression with replicate measurements. 17. Schneeweill, H. J. (1986). Lineare Modelle mit fehlerbehafteten Daten.
81, 624-629. 12. McCullagh, P. (1991). Quasi-likelihood and estimating functions. V. Hinkley, N. J. ). Chapman and Hall, London. 13. S. F. (1995) Polynomial measurement error modelling. Computational Statistics & Data Analysis. 19, 1-21. 14. C. and Spiegelman, D. (1989). Correction of logistic regression relative risk estimates and confidence intervals for systematic withinperson measurement error. Statistics in Medicine. 8, 1051-1069. f:;: 44 Markus Thamerus 15. W. (1993). Likelihood analysis for probit regression with measurement error.