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Book reviews

Heyer, H.; Elworthy, K.; Cressie, N.; Williams, R.; … - In: Metrika 42 (1995) 1, pp. 139-148

Persistent link: https://www.econbiz.de/10005375752

A coefficient of stability of rank tests in the one-sample case

Büning, H. - In: The statistician : journal of the Institute of Statisticians 42 (1993) 2, pp. 175-180

Persistent link: https://www.econbiz.de/10006007716

Robust and adaptive tests for the two-sample location problem

Büning, H. - In: Operations-Research-Spektrum : Zeitschrift der … 16 (1994) 1, pp. 33-40

Persistent link: https://www.econbiz.de/10006868210

Mean square error matrix superiority of estimators under linear restrictions and misspecification

Wijekoon, P.; Trenkler, G. - In: Economics Letters 30 (1989) 2, pp. 141-149

Persistent link: https://www.econbiz.de/10005296660

The Moore-Penrose Inverse of a Symmetric Matrix

Trenkler, G.; Magnus, Jan R. - In: Econometric Theory 8 (1992) 04, pp. 585-586

Persistent link: https://www.econbiz.de/10008739829

Literatur - Moderne Matrix Algebra mit Anwendungen in der Statistik

Schmidt, K.; Trenkler, G.; Uebe, G. - In: Allgemeines statistisches Archiv : AStA ; journal of … 83 (1999) 4

Persistent link: https://www.econbiz.de/10006571948

Characterization of a Projector

Farebrother, R.W.; Trenkler, G. - In: Econometric theory 9 (1993) 4, pp. 690

Persistent link: https://www.econbiz.de/10007019254

Characterization of an Orthogonal Projection Matrix

Farebrother, R.W.; Pordzik, P.; Trenkler, G. - In: Econometric theory 9 (1993) 3, pp. 523

Persistent link: https://www.econbiz.de/10007019279

Characterization of a Projector

Farebrother, R.W.; Trenkler, G. - In: Econometric Theory 9 (1993) 04, pp. 689-689

Persistent link: https://www.econbiz.de/10004987268

Nonnegative Minimum Biased Quadratic Estimation in the Linear Regression Models

Gnot, S.; Trenkler, G.; Zmyslony, R. - In: Journal of Multivariate Analysis 54 (1995) 1, pp. 113-125

In the paper the problem of nonnegative estimation of [beta]'H[beta] + h[sigma]2 in the linear model E(y) = X[beta], Var(y)= [sigma]2I is discussed. Here H is a nonnegative definite matrix while h is a nonnegative scalar. An iterative procedure for the nonnegative minimum biased quadratic...

Persistent link: https://www.econbiz.de/10005160513