https://libpls.net/publication/PLS_basic_2001.pdf

PLS-regression: a basic tool of chemometrics

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

/pdf/handbook-of-fingerprint-recognition-16mh887j24.pdf

Handbook of Fingerprint Recognition

Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.

How iris recognition works

The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.

/pdf/how-iris-recognition-works-z3q26daq53.pdf

On propose une statistique du rapport de vraisemblance construite a partir de la distribution conditionnelle des observations, etant donne les estimateurs du maximum de vraisemblance pour les parametres de nuisance

https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1987.tb01422.x

Parameter Orthogonality and Approximate Conditional Inference

A conditional method of inference is used to derive exact confidence intervals for several life characteristics such as location, scale, quantiles, and reliability when the data are Type II progressively censored. The method is shown to be feasible and practical, although a computer program may be required for its implementation. The method is applied for the purpose of illustration to the extreme-value and the one- and two-parameter exponential models. Prediction limits for the lifelength of future units are also discussed. An example consisting of data from an accelerated test on insulating fluid reported by Nelson is used for illustration and comparison.

Interval Estimation of Parameters of Life From Progressively Censored Data

A set of frameworks for latent variable multivariate regression method is developed. The first two of these frameworks describe the objective functions satisfied by the latent variables chosen in canonical coordinates regression (CCR), reduced rank regression (RRR) and SIMPLS. These frameworks show the methods as a natural progression from CCR (maximizing correlation) to SIMPLS (maximizing covariance) via RRR (which is an intermediate method). These frameworks are unique in that they look at these methods in terms of latent variables in both the X- and Y-spaces. This adds insight to the nature of the latent variables being chosen. These frameworks are then extended to include PLS for latent variables beyond the first component. This new framework provides a detailed description of the objective function satisfied by PLS latent variables for the multivariate case. It also includes CCR, RRR and SIMPLS, allowing comparisons between the methods. A further framework suggests a new method, undeflated PLS (UDPLS), which adds insight to the effect of the deflation process on PLS. The impact of the objective functions on each of the methods is illustrated on real data from a mineral sorting plant.

Frameworks for latent variable multivariate regression

Latent variable multivariate regression modeling

New results on start-up demonstration tests are obtained. In particular, the average length of a demonstration test is derived and inferences based on demonstration test data for the probability p of a successful start-up are discussed. Practical recomm..

Roman Viveros

Papers

Interval Estimation of Parameters of Life From Progressively Censored Data

Frameworks for latent variable multivariate regression

Latent variable multivariate regression modeling

Statistical inference from start-up demonstration test data

Sooner and later waiting time problems for Markovian Bernoulli trials