A modified Kolmogorov-Smirnov test for a rectangular distribution with unknown parameters: Computation of the distribution of the test statistic

1999 ◽  
Vol 40 (3) ◽  
pp. 343-349 ◽  
Author(s):  
Helmut Schellhaas
2006 ◽  
Vol 76 (3) ◽  
pp. 195-206 ◽  
Author(s):  
Michael D. Weber ◽  
Lawrence M. Leemis ◽  
Rex K. Kincaid

2021 ◽  
Vol 11 (11) ◽  
pp. 5073
Author(s):  
Gianluigi Caccianiga ◽  
Gérard Rey ◽  
Paolo Caccianiga ◽  
Alessandro Leonida ◽  
Marco Baldoni ◽  
...  

Peri-implantitis management could be performed either with a surgical or non-surgical approach to the implant surfaces. Various treatment options have been proposed in the literature, such as antiseptic and antibiotic therapies, chemical agents, curettes, ultrasonic, air abrasive, rotary titanium brushes and laser treatments; in particular, photodynamic therapy combined with hydrogen peroxide (OHLLT) has proved to be efficient in the removal of bacterial biofilm from implant surfaces. The aim of our study is to compare OHLLT performed with a surgical approach to a non-surgical approach. We selected a cluster of 227 implants affected by peri-implantitis: 139 implants were treated with a non-surgical approach and 88 implants with a surgical approach. Bone loss pre-operative and post-operative (with a follow-up of five years) have been registered. The collected data were entered on the Statistical Package for Social Sciences (SPSS) version 11.5. The results indicate a statistically significant difference between the two groups, with a mean bone loss after treatment of 2.3 mm for OHLLT with a surgical approach and 3.8 mm for OHLLT with a non-surgical approach; according to the Kolmogorov–Smirnov test, the overall data followed a normal distribution (value of the Kolmogorov–Smirnov test statistic = 0.0891; p = 0.35794). Thus, a surgical approach in the case of peri-implantitis seems to be more effective, probably due to the possibility of a deeper sanitization of implant surfaces, hardly reachable with only non-surgical approach.


2021 ◽  
Author(s):  
Thalis D. Galeno ◽  
João Gama ◽  
Douglas O. Cardoso

Motivated by the challenges of Big Data, this paper presents an approximative algorithm to assess the Kolmogorov-Smirnov test. This goodness of fit statistical test is extensively used because it is non-parametric. This work focuses on the one-sample test, which considers the hypothesis that a given univariate sample follows some reference distribution. The method allows to evaluate the departure from such a distribution of a input stream, being space and time efficient. We show the accuracy of our algorithm by making several experiments in different scenarios: varying reference distribution and its parameters, sample size, and available memory. The performance of rival methods, some of which are considered the state-of-the-art, were compared. It is demonstrated that our algorithm is superior in most of the cases, considering the absolute error of the test statistic.


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