Bootstrap-based tolerance intervals for application to method validation

Rebafka, Tabea; Clémençon, Stephan; Feinberg, Max

doi:10.1016/j.chemolab.2007.06.001

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…Selection of a proper number of PLS LVs has been a subject of interest to chemometricians developing new techniques [59][60][61][62][63][64][65][66][67][68][69][70][71] or comparing existing techniques [72][73][74][75][76]. To assess the quality of a calibration model, standard statistical measures are normally applied.…”

Section: Model Complexitymentioning

confidence: 99%

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Multivariate calibration of NIR spectroscopic sensors for continuous glucose monitoring

Goodarzi

Sharma

Ramón

et al. 2015

TrAC Trends in Analytical Chemistry

124

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Diabetes, a disorder in the control of blood-glucose levels, is one of the most serious metabolic diseases worldwide. Among the investigated technologies for continuous glucose monitoring (CGM), near-infrared spectroscopy (NIR) has received the most attention. There have been many attempts to develop NIR-based CGM systems with promising in-vitro results, but they lacked robustness for in-vivo use. We critically review the application of chemometrics for CGM and the research needed. Pre-processing and multivariate-calibration techniques, which allow exploiting expert knowledge on the potential interferences, are possible solutions. The combination and first overtone bands in the ranges 2050-2300 nm and 1500-1800 nm, respectively, are the most informative regions. We therefore recommended selecting the most informative variables and exploiting the available expert knowledge on known interferences in pre-processing or multivariate calibration to develop an NIR-based CGM sensor for in-vivo use.

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Section: Model Complexitymentioning

confidence: 99%

“…Typically, the model complexity is tuned based on the minimization of the prediction error in the validation. The most used techniques are based on cross validation (CV) [76], Monte Carlo CV (MCCV) [60][61][62][63][64], randomization [65,66] and bootstrapping [67][68][69][70][71].…”

Section: Model Complexitymentioning

confidence: 99%

Multivariate calibration of NIR spectroscopic sensors for continuous glucose monitoring

Goodarzi

Sharma

Ramón

et al. 2015

TrAC Trends in Analytical Chemistry

124

View full text Add to dashboard Cite

show abstract

“…For this, the iterative bootstrap approach of Fernholz and Gillespie () proposed for univariate data may be adapted by taking the tolerance factor from our method as an initial guess. Another possibility may be to use a nested bootstrap step, along the lines of Rebafka, Clémençon, and Feinberg (), to estimate the content of a given interval nonparametrically rather than estimating it using that assumes normality. However, the last two approaches add another layer of bootstrapping, making them even more computationally demanding than the proposed procedure.…”

Section: Discussionmentioning

confidence: 99%

Tolerance Bands for Functional Data

Rathnayake

Choudhary

2015

Biometrics

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Often the object of inference in biomedical applications is a range that brackets a given fraction of individual observations in a population. A classical estimate of this range for univariate measurements is a "tolerance interval." This article develops its natural extension for functional measurements, a "tolerance band," and proposes a methodology for constructing its pointwise and simultaneous versions that incorporates both sparse and dense functional data. Assuming that the measurements are observed with noise, the methodology uses functional principal component analysis in a mixed model framework to represent the measurements and employs bootstrapping to approximate the tolerance factors needed for the bands. The proposed bands also account for uncertainty in the principal components decomposition. Simulations show that the methodology has, generally, acceptable performance unless the data are quite sparse and unbalanced, in which case the bands may be somewhat liberal. The methodology is illustrated using two real datasets, a sparse dataset involving CD4 cell counts and a dense dataset involving core body temperatures.

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“…Other approaches are based on the so-called accuracy profile constructed by use of the ␤-expectation tolerance interval [47,49,63,64]. This approach, however, is still under development and regularly refined [46,65,66]. Boiled down, the concepts recommend to consider ␣-and ␤-errors and tolerance intervals in method validation studies.…”

Section: Significance Testingmentioning

confidence: 98%