It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex

Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition

In randomized experiments, treatment and control groups should be roughly the same—balanced—in their distributions of pre- treatment variables. But how nearly so? Can descriptive comparisons meaningfully be paired with significance tests? If so, should there be several such tests, one for each pretreatment variable, or should there be a single, omnibus test? Could such a test be engineered to give eas- ily computed p-values that are reliable in samples of moderate size, or would simulation be needed for reliable calibration? What new con- cerns are introduced by random assignment of clusters? Which tests of balance would be optimal? To address these questions, Fisher's randomization inference is ap- plied to the question of balance. Its application suggests the reversal of published conclusions about two studies, one clinical and the other a field experiment in political participation.

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Covariate balance in simple, stratified and clustered comparative studies

A balanced sampling design is defined by the property that the Horvitz-Thompson estimators of the population totals of a set of auxiliary variables equal the known totals of these variables. Therefore the variances of estimators of totals of all the variables of interest are reduced, depending on the correlations of these variables with the controlled variables. In this paper, we develop a general method, called the cube method, for selecting approximately balanced samples with equal or unequal inclusion probabilities and any number of auxiliary variables.

/pdf/efficient-balanced-sampling-the-cube-method-3p7kld5x2u.pdf

Efficient balanced sampling: The cube method

Sources of Demographic Data.- Sampling Designs and Inference.- Waiting Times and Their Statistical Estimation.- Regression Models for Counts and Survival.- Multistate Models and Cohort-Component Book-Keeping.- Approaches to Forecasting Demographic Rates.- Uncertainty in Demographic Forecasts: Concepts, Issues, and Evidence.- Statistical Propagation of Error in Forecasting.- Errors in Census Numbers.- Financial Applications.- Decision Analysis and Small Area Estimates.

http://bio2013.persiangig.com/document/TIME%20SERIES/Statistical%20Demography%20and%20Forecasting.pdf

Statistical demography and forecasting

A simple method to select a spatially balanced sample using equal or unequal inclusion probabilities is presented. For populations with spatial trends in the variables of interest, the estimation can be much improved by selecting samples that are well spread over the population. The method can be used for any number of dimensions and can hence also select spatially balanced samples in a space spanned by several auxiliary variables. Analysis and examples indicate that the suggested method achieves a high degree of spatial balance and is therefore efficient for populations with trends.

Ronaldo Iachan

Papers

Sampling from a finite population