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## Prediction by conditional simulation: models and algorithms

### Space, Structure and Randomness (2005-01-01) 183: 39-68 , January 01, 2005

Prediction here refers to the behavior of a regionalized variable: average ozone concentration in April 2004 in Paris, maximum lead concentration in an industrial site, recoverable reserves of an orebody, breakthrough time from a source of pollution to a target, etc. Dedicating a whole chapter of a book in honor to Georges Matheron to prediction by conditional simulation is somewhat paradoxical. Indeed performing simulations requires strong assumptions, whereas Matheron did his utmost to weaken the prerequisites for the prediction methods he developed. Accordingly, he never used them with the aim of predicting and they represented a marginal part of his activity. The turning bands method, for example, is presented very briefly in a technical report on the Radon transform to illustrate the one-to-one mapping between *d*-dimensional isotropic covariances and unidimensional covariances^{1} [44]. As for the technique of conditioning by kriging, it is nowhere to be found in Matheron’s entire published works, as he merely regarded it as an immediate consequence of the orthogonality of the kriging estimator and the kriging error.

## An Item Response Theory Model for Student Ability Evaluation Using Computer-Automated Test Results

### New Developments in Classification and Data Analysis (2005-01-01): 325-332 , January 01, 2005

The aim of this paper is to evaluate the student learning about Computer Science subjects. A questionnaire based on ordinal scored items has been submitted to the students through a computer automated system. The data collected have been analyzed by using a latent variable model for ordinal data within the Item Response Theory framework. The scores obtained from the model allow to classify the students according to the reached competence.

## A new instrumental variable estimation for diffusion processes

### Annals of the Institute of Statistical Mathematics (2005-12-01) 57: 733-745 , December 01, 2005

We consider the problem of parametric inference from continuous sample paths of the diffusion processes {*x(t)}* generated by the system of possibly nonstationary and/or nonlinear Ito stochastic differential equations. We propose a new instrumental variable estimator of the parameter whose pivotal statistic has a Gaussian distribution for all possible values of parameter. The new estimator enables us to construct exact level-α confidence intervals and tests for the parameter in the possibly non-stationary and/or nonlinear diffusion processes. Applications to several non-stationary and/or nonlinear diffusion processes are considered as examples.

## Functional Data Objects and Operations

### S+ Functional Data Analysis (2005-01-01): 45-69 , January 01, 2005

## Front Matter - The Evaluation of Surrogate Endpoints

### The Evaluation of Surrogate Endpoints (2005-01-01) , January 01, 2005

## A note on D-optimal designs for models with and without an intercept

### Statistical Papers (2005-07-01) 46: 451-458 , July 01, 2005

In this paper we give a sufficient condition under which the*D*-optimal design for a regression model without an intercept can be obtained from the*D*-optimal design for the corresponding model with an intercept by simply removing the origin from its support points. Examples are given to demonstrate the applications of the results.

## Partial Differential Equations for Morphological Operators

### Space, Structure and Randomness (2005-01-01) 183: 369-390 , January 01, 2005

Two of G. Matheron’s seminal contributions have been his development of size distributions (else called ‘granulometries’) and his kernel representation theory. The first deals with semigroups of multiscale openings and closings of binary images (shapes) by compact convex sets, a basic ingredient of which are the multiscale Minkowski dilations and erosions. The second deals with representing increasing and translation-invariant set operators as union of erosions by its kernel sets or as an intersection of dilations.

## Quality Control and Good Epidemiological Practice

### Handbook of Epidemiology (2005-01-01): 503-556 , January 01, 2005

The use of data is fundamental in epidemiology. Epidemiologic research on causation uses data in a search for the true nature of the relationship between exposure and disease. Similarly, research on the consequences of interventions seeks an unbiased characterization of the effects of independently varying factors on the outcome measure(s). One of the most rewarding moments for a researcher is obtaining the preliminary results from his or her study. However, the question “do I believe what I see?” should immediately come to mind. The answer to this question is determined in large part by the more mundane but critical question of how good is the quality of the data, rather than by the elegance of the scientific method. Errors that occur during study population selection or in the measurement of study exposures, outcomes, or covariates can lead to a biased estimate of the effect of exposure on risk for the disease of interest. Misclassification of exposure or disease that occurs randomly between all study participants decreases the power of the study to detect an association where it exists. Data collection that is differentially biased may have more severe consequences, and can lead to an incorrect assessment of the relationship between exposure and disease.