The influence of autocorrelation in signature extraction: An example from a geobotanical investigation of Cotter Basin, Montana

The presence of positive serial correlation (autocorrelation) in remotely sensed data results in an underestimate of the variance-covariance matrix when calculated using contiguous pixels. This underestimate produces an inflation in F statistics. For a set of Thematic Mapper Simulator data (TMS), used to test the ability to discriminate a known geobotanical anomaly from its background, the inflation in F statistics related to serial correlation is between 7 and 70 times. This means that significance tests of means of the spectral bands initially appear to suggest that the anomalous site is very different in spectral reflectance and emittance from its background sites. However, this difference often disappears and is always dramatically reduced when compared to frequency distributions of test statistics produced by the comparison of simulated training sets possessing equal means, but which are composed of autocorrelated observations

A note on prognostic accuracy evaluation of regression models applied to longitudinal autocorrelated binary data

Background: Focus of this work was on evaluating the prognostic accuracy of two approaches for modelling binary longitudinal outcomes, a Generalized Estimating Equation (GEE) and a likelihood based method, Marginalized Transition Model (MTM), in which a transition model is combined with a marginal generalized linear model describing the average response as a function of measured predictors. Methods: A retrospective study on cardiovascular patients and a prospective study on sciatic pain were used to evaluate discrimination by computing the Area Under the Receiver-Operating-Characteristics curve, (AUC ), the Integrated Discrimination Improvement (IDI) and the Net Reclassification Improvement (NRI) at different time occasions. Calibration was also evaluated. A simulation study was run in order to compare model’s performance in a context of a perfect knowledge of the data generating mechanism. Results: Similar regression coefficients estimates and comparable calibration were obtained; an higher discrimination level for MTM was observed. No significant differences in calibration and MSE (Mean Square Error) emerged in the simulation study; MTM higher discrimination level was confirmed. ConclusionS: The choice of the regression approach should depend on the scientific question being addressed: whether the overall population-average and calibration are the objectives of interest, or the subject-specific patterns and discrimination. Moreover, some recently proposed discrimination indices are useful in evaluating predictive accuracy also in a context of longitudinal studies

"Racial Preferences in a Small Urban Housing Market: A Spatial Econometric Analysis of Microneighborhoods in Kingston, New York"

This paper use spatial econometric models to test for racial preferences in a small urban housing market. Identifying racial preferences is difficult when unobserved neighborhood amenities vary systematically with racial composition. We adopt three strategies to redress this problem: (1) we focus on housing price differences across microneighborhoods in the small and relatively homogenous city of Kingston, New York; (2) we introduce GIS-based spatial amenity variables as controls in the hedonic regressions; and (3) we use spatial error and lag models to explicitly account for the spatial dependence of unobserved neighborhood amenities. Our simple OLS estimates agree with the consensus in the literature that black neighborhoods have lower housing prices. However, racial price discounts are no longer significant when we account for the spatial dependence of errors. Our results suggest that price discounts in black neighborhoods are caused not by racial preferences but by the demand for amenities that are typically not found in black neighborhoods.Housing; Race; Neighborhood Amenities; Spatial Econometrics Commonwealth of Independent States

A note on prognostic accuracy evaluation of regression models applied to longitudinal autocorrelated binary data

Background: Focus of this work was on evaluating the prognostic accuracy of two approaches for modelling binary longitudinal outcomes, a Generalized Estimating Equation (GEE) and a likelihood based method, Marginalized Transition Model (MTM), in which a transition model is combined with a marginal generalized linear model describing the average response as a function of measured predictors. Methods: A retrospective study on cardiovascular patients and a prospective study on sciatic pain were used to evaluate discrimination by computing the Area Under the Receiver-Operating-Characteristics curve, (AUC), the Integrated Discrimination Improvement (IDI) and the Net Reclassification Improvement (NRI) at different time occasions. Calibration was also evaluated. A simulation study was run in order to compare model’s performance in a context of a perfect knowledge of the data generating mechanism. Results: Similar regression coefficients estimates and comparable calibration were obtained; an higher discrimination level for MTM was observed. No significant differences in calibration and MSE (Mean Square Error) emerged in the simulation study, that instead confirmed the MTM higher discrimination level. Conclusions: The choice of the regression approach should depend on the scientific question being addressed, i.e. if the overall population-average and calibration or the subject-specific patterns and discrimination are the objectives of interest, and some recently proposed discrimination indices are useful in evaluating predictive accuracy also in a context of longitudinal studie

Earnings mobility and measurement error : a pseudo-panel approach

The degree of mobility in incomes is often seen as an important measure of the equality of opportunity in a society and of the flexibility and freedom of its labor market. But estimation of mobility using panel data is biased by the presence of measurement error and non-random attrition from the panel. This paper shows that dynamic pseudo-panel methods can be used to consistently estimate measures of absolute and conditional mobility in the presence of non-classical measurement errors. These methods are applied to data on earnings from a Mexican quarterly rotating panel. Absolute mobility in earnings is found to be very low in Mexico, suggesting that the high level of inequality found in the cross-section will persist over time. However, the paper finds conditional mobility to be high, so that households are able to recover quickly from earnings shocks. These findings suggest a role for policies which address underlying inequalities in earnings opportunities.Inequality,Housing&Human Habitats,Roads&Highways,Economic Theory&Research,Rural Poverty Reduction

Poor identification and estimation problems in panel data models with random effects and autocorrelated errors

A dramatically large number of corner solutions occur when estimating by (Gaussian) maximum likelihood a simple model for panel data with random effects and autocorrelated errors. This can invalidate results of applications to panel data with a short time dimension, even in a correctly specified model. We explain this unpleasant effect (usually underestimated, almost ignored in the literature) showing that the expected log-likelihood is nearly flat, thus rising problems of poor identification.panel data, maximum likelihood, identification.

Technical support for creating an artificial intelligence system for feature extraction and experimental design

Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied