ENBIS-21 Online Conference

Session

Quality 4

PS75_C20

15 Sept 2021, 14:00

Online

Conveners

Quality 4

• Chair: Shirley Coleman

7. Outlier detection in sensor networks

Mr Martial AMOVIN-ASSAGBA (Arpege Master K / Université de Lyon, Lyon 2, ERIC UR 3083 )

15/09/2021, 14:00

Metrology & measurement systems analysis

Emerging technologies ease the recording and collection of high frequency data produced by sensor networks. From a statistical point of view, these data can be view as discrete observations of random functions. Our industrial goal is to detect abnormal measurement. Statistically, it consists in detecting outliers in a multivariate functional data set.
We propose a robust procedure based on...

45. Outliers and the instrumental variables estimator in the linear regression model with endogeneity

Dr Aleš Toman (School of Economics and Business, University of Ljubljana)

15/09/2021, 14:20

Economics

In a linear regression model, endogeneity (i.e., a correlation between some explanatory variables and the error term) makes the classical OLS estimator biased and inconsistent. When instrumental variables (i.e., variables that are correlated with the endogenous explanatory variables but not with the error term) are available to partial out endogeneity, the IV estimator is consistent and widely...

129. Robust bootstraped h and k Mandel’s statistics for outlier detection in Interlaboratory Studies

Génesis Moreno (Escuela Politécnica Nacional), Cristian Solorzano (Escuela Politécnica Nacional)

15/09/2021, 14:40

Quality

A new methodology based on bootstrap resampling techniques is proposed to estimate the distribution of the h and k Mandel's statistics, commonly applied to identify laboratories that supply inconsistent results usually utilized to detect those outlier laboratories by testing the hypothesis of reproducibility and repeatability (R & R), in the framework of Interlaboratory Studies (ILS)....