@misc{10481/74505,
year = {2022},
month = {4},
url = {http://hdl.handle.net/10481/74505},
abstract = {Our practical motivation is the analysis of potential correlations between spectral noise current and threshold voltage from
common on-wafer MOSFETs. The usual strategy leads to the use of standard techniques based on Normal linear regression
easily accessible in all statistical software (both free or commercial). However, these statistical methods are not appropriate
because the assumptions they lie on are not met. More sophisticated methods are required. A new strategy based on the most
novel nonparametric techniques which are data-driven and thus free from questionable parametric assumptions is proposed.
A backfitting algorithm accounting for random effects and nonparametric regression is designed and implemented. The
nature of the correlation between threshold voltage and noise is examined by conducting a statistical test, which is based on
a novel technique that summarizes in a color map all the relevant information of the data. The way the results are presented
in the plot makes it easy for a non-expert in data analysis to understand what is underlying. The good performance of the
method is proven through simulations and it is applied to a data case in a field where these modern statistical techniques are
novel and result very efficient.},
organization = {Spanish Government	RTI2018-099723-B-I00	
PID2020-120217RB-I00},
organization = {Junta de Andalucia	B-FQM-284-UGR20	
B-CTS-184-UGR20},
organization = {IMAG-Maria de Maeztu grant	CEX2020-001105-/AEI},
publisher = {Springer},
keywords = {1/f Noise},
keywords = {Backfitting algorithm},
keywords = {Bootstrap},
keywords = {MOSFET},
keywords = {SiZer map},
keywords = {Statistical modeling},
title = {Statistical supervised learning with engineering data: a case study of low frequency noise measured on semiconductor devices},
doi = {10.1007/s00170-022-08949-z},
author = {Gámiz Pérez, María Luz and Raya Miranda, Rocío},
}