From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

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Neural networks for pattern recognition

This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.

/pdf/a-simple-positive-semi-definite-heteroskedasticity-and-1h9nwsy8i5.pdf

A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix

Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.

PLS-SEM: Indeed a Silver Bullet

Generalized Additive Models.

Donoho and Johnstone (1994) proposed a method for reconstructing an unknown function f on [0,1] from noisy data d/sub i/=f(t/sub i/)+/spl sigma/z/sub i/, i=0, ..., n-1,t/sub i/=i/n, where the z/sub i/ are independent and identically distributed standard Gaussian random variables. The reconstruction f/spl circ/*/sub n/ is defined in the wavelet domain by translating all the empirical wavelet coefficients of d toward 0 by an amount /spl sigma//spl middot//spl radic/(2log (n)/n). The authors prove two results about this type of estimator. [Smooth]: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures. [Adapt]: the estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. The present proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model. >

De-noising by soft-thresholding

Hydrogen Bonding of Fluorenide by a Hydroxy Proton Directly Observed by UV, NMR and IR Spectroscopy.

Described are computer-based methods and apparatuses, including computer program products, for monitoring, detecting, and quantifying chemical compounds in a sample. A sample measurement comprising a digitized spectroscopic profile is received. A multivariate multistage background model comprising a first model that models a first time effect, a second model that models a second time effect that is different than the first time effect, or both of the above two. A background corrected sample measurement based on the sample measurement and the multivariate multistage background model is generated. A multivariate multistage library search, fault detection, and quantification algorithm is executed to identify one or more primary chemicals in the background corrected sample measurement. The search, detection, and quantification algorithm includes identifying one or more candidate chemicals in the background corrected sample measurement based on a multivariate statistical process control and identifying and quantifying a first primary chemical based on a focused chemical evaluation of the one or more candidate chemicals.

Monitoring, detecting and quantifying chemical compounds in sample

INLR (Implicit Non‐linear Latent variable Regression). II. Blockscaling of Expanded Terms with QSAR Examples

Utilisation d'un modele d'etalonnage des moindres carres partiels pour l'analyse quantitative de donnees bilineaires

Data reduction of bilinear matrixes prior to calibration

Principal component (p.c.) analysis combined with a cross-validation technique (c.v.) was used in the analysis of some data sets composed of solvatochromic shift data for non-hydrogen bonding indicators. The data were basically the same as earlier used by Kamlet et al. to define the π* solvent polarity scale. The c.v. showed that to adequately describe the data a two-component p.c. model was needed and consequently two parameters θ1k and θ2k were determined for each solvent k. The performance of these solvent parameters was compared with the π* scale.

Svante Wold

Papers

Hydrogen Bonding of Fluorenide by a Hydroxy Proton Directly Observed by UV, NMR and IR Spectroscopy.

Monitoring, detecting and quantifying chemical compounds in sample

INLR (Implicit Non‐linear Latent variable Regression). II. Blockscaling of Expanded Terms with QSAR Examples

Data reduction of bilinear matrixes prior to calibration

Statistical analysis of solvatochromic shift data