Contributions to accounting, auditing and internal control : essays in honour of professor Teija Laitinen

Huomautus kielistä: Tekstiä suomeksi ja englanniksiKirjoittajat: Bürkland Sirle, Gullkvist Benita, Kallio Minna, Back Barbro, Kihn Lili, Näsi Salme, Koskela Merja, Pilke Nina, Laaksonen Pirjo, Jyrinki Henna, Morton Anja, Myllymäki Emma-Riikka, Jokipii Annukka, Niskanen Mervi, Virtanen Ailafi=vertaisarvioitu|en=peerReviewed

Mining Taxation Data with Parallel BMARS

A new parallel version of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm is discussed. By partitioning the data over the processors of a parallel computational system one achieves good parallel eciency. Instead of using truncated power basis functions of the original MARS, the new method (BMARS) utilises B-splines which improves numerical stability and reduces the computational cost of the procedure. In addition, the coecients of the basis functions of a BMARS model provide quickly accessible information about the local behaviour of the function. The algorithm has a time complexity proportional to the number of data records. The method provides a new means for the detection of areas in the space of features which are characterised by the \interesting" patterns of response values. This is applied to searching for classes of incorrect tax returns using multiple predictor variables or features. Th