There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Statistics for Spatial Data.

On robust estimation of the location parameter

Remaining useful life estimation - A review on the statistical data driven approaches

Permanent Scatterer SAR Interferometry (PSInSAR) aims to identify coherent radar targets exhibiting high phase stability over the entire observation time period. These targets often correspond to point-wise, man-made objects widely available over a city, but less present in non-urban areas. To overcome the limits of PSInSAR, analysis of interferometric data-stacks should aim at extracting geophysical parameters not only from point-wise deterministic objects (i.e., PS), but also from distributed scatterers (DS). Rather than developing hybrid processing chains where two or more algorithms are applied to the same data-stack, and results are then combined, in this paper we introduce a new approach, SqueeSAR, to jointly process PS and DS, taking into account their different statistical behavior. As it will be shown, PS and DS can be jointly processed without the need for significant changes to the traditional PSInSAR processing chain and without the need to unwrap hundreds of interferograms, provided that the coherence matrix associated with each DS is properly “squeezed” to provide a vector of optimum (wrapped) phase values. Results on real SAR data, acquired over an Alpine area, challenging for any InSAR analysis, confirm the effectiveness of this new approach.

A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR

Preface. 1. Introduction. 2. Probability Basics. 3. Statistics Basics. 4. Bayesian Statistics. 5. Order Statistics. 6. Goodness of Fit. 7. Rank Tests. 8. Designed Experiments. 9. Categorical Data. 10. Estimating Distribution Functions. 11. Density Estimation. 12. Beyond Linear Regression. 13. Curve Fitting Techniques. 14. Wavelets. 15. Bootstrap. 16. EM Algorithm. 17. Statistical Learning. 18. Nonparametric Bayes. A. MATLAB. B. WinBUGS. MATLAB Index. Author Index. Subject Index.

Nonparametric Statistics with Applications to Science and Engineering (Wiley Series in Probability and Statistics)

/pdf/nonparametric-statistics-with-applications-to-science-and-3kp1pp9p09.pdf

Nonparametric statistics with applications to science and engineering

https://scholarship.richmond.edu/cgi/viewcontent.cgi?article=1209&context=mathcs-faculty-publications

Degradation models and implied lifetime distributions

As an alternative to traditional life testing, degradation tests can be effective in assessing product reliability when measurements of degradation leading to failure can be observed. This article presents a degradation model for highly reliable light displays, such as plasma display panels and vacuum fluorescent displays (VFDs). Standard degradation models fail to capture the burn-in characteristics of VFDs, when emitted light actually increases up to a certain point in time before it decreases (or degrades) continuously. Random coefficients are used to model this phenomenon in a nonlinear way, which allows for a nonmonotonic degradation path. In many situations, the relative efficiency of the lifetime estimate is improved over the standard estimators based on transformed linear models.

/pdf/a-nonlinear-random-coefficients-model-for-degradation-2tuwe7i0j7.pdf

A Nonlinear Random-Coefficients Model for Degradation Testing

An experiment was carried out to investigate the long-term effects of active learning methods on student retention in an introductory engineering statistics class. Two classes of students participated in the study—one class was taught using traditional lecture-based learning, and the other class stressed group projects and cooperative learning-based methods. Retention was measured by examining the students immediately after the course finished, and then again eight months later. The findings suggest that active learning can help to increase retention for students with average or below average scores. Graphical displays of the data, along with standard statistical analyses, help explain the observed difference in retention between students in the two different learning environments.

https://www.jstor.org/stable/info/2686032

Paul H. Kvam

Papers

Nonparametric Statistics with Applications to Science and Engineering (Wiley Series in Probability and Statistics)

Nonparametric statistics with applications to science and engineering

Degradation models and implied lifetime distributions

A Nonlinear Random-Coefficients Model for Degradation Testing

The Effect of Active Learning Methods on Student Retention in Engineering Statistics