Statistics for Spatial Data.

Statistical Analysis With Missing Data (2nd ed.) (Book)

Clinical Trials: A Practical Approach

Statistics For Spatial Data

Survival Analysis: Techniques for Censored and Truncated Data

[1] Empirical evidence of the preearthquake ionospheric anomalies (PEIAs) is reported by statistically investigating the relationship between variations of the plasma frequency at the ionospheric F2 peak foF2 and 184 earthquakes with magnitude M ≥ 5.0 during 1994–1999 in the Taiwan area. The PEIA, defined as the abnormal decrease more than about 25% in the ionospheric foF2 during the afternoon period, 1200–1800 LT, significantly occurs within 5 days before the earthquakes. Moreover, the odds of earthquakes with PEIA increase with the earthquake magnitude but decrease with the distance from the epicenter to the ionosonde station. These results indicate that the PEIA is energy related.

http://www.stat.ncu.edu.tw/teacher/YIChen/File3-16.pdf

A statistical investigation of preearthquake ionospheric anomaly

The anomalous depression of the maximum plasma frequency in the ionosphere (foF2) appears significantly within 1-5 days before the M ≥5.0 earthquakes in the Taiwan area during 1994 - 1999 (Liu et al. 2003). In this paper, we propose two statistical tests against the foF2 anomaly as a candidate for a precursor of earthquakes based on criteria including the success rate, alarm rate, probability gain, and R score. One statistical test is conducted to investigate the significance of the observed foF2 anomalies related to the recorded M ≥5.0 earthquakes in the Taiwan area during 1994- 1999. The other statistical test is designed to compare the foF2 anomaly- based method with competitive alternatives for predicting the earthquakes under study. The involved alternatives are a naive prediction based on a coin-tossing experiment and a simple prediction method constructed from the current M ≥5.0 earthquakes catalog during 1994 - 1999. The simulation results indicate that, contrast to possible foF2 anomalies, the observed foF2 anomalies are significantly earthquake-related. Moreover, comparing with the alternative predictions under study, the foF2 anomaly remains valid for temporal alarming of the M ≥5.0 earthquakes in the Taiwan area during 1994 - 1999.

/pdf/statistical-tests-for-pre-earthquake-ionospheric-anomaly-wopkw71izj.pdf

Statistical tests for pre-earthquake ionospheric anomaly

In many fields of science, predicting variables of interest over a study region based on noisy data observed at some locations is an important problem. Two popular methods for the problem are kriging and smoothing splines. The former assumes that the underlying process is stochastic, whereas the latter assumes it is purely deterministic. Kriging performs better than smoothing splines in some situations, but is outperformed by smoothing splines in others. However, little is known regarding selecting between kriging and smoothing splines. In addition, how to perform variable selection in a geostatistical model has not been well studied. In this article we propose a general methodology for selecting among arbitrary spatial prediction methods based on (approximately) unbiased estimation of mean squared prediction errors using a data perturbation technique. The proposed method accounts for estimation uncertainty in both kriging and smoothing spline predictors, and is shown to be optimal in terms of two mean sq...

https://www.jstor.org/stable/info/27639942

Optimal Geostatistical Model Selection

Variable selection in geostatistical regression is an important problem, but has not been well studied in the literature. In this paper, we focus on spatial prediction and consider a class of conditional information criteria indexed by a penalty parameter. Instead of applying a fixed criterion, which leads to an unstable predictor in the sense that it is discontinuous with respect to the response variables due to that a small change in the response may cause a different model to be selected, we further stabilize the predictor by local model averaging, resulting in a predictor that is not only continuous but also differentiable even after plugging-in estimated model parameters. Then Stein’s unbiased risk estimate is applied to select the penalty parameter, leading to a data-dependent penalty that is adaptive to the underlying model. Some numerical experiments show superiority of the proposed model averaging method over some commonly used variable selection methods. In addition, the proposed method is applied to a mercury data set for lakes in Maine.

/pdf/geostatistical-model-averaging-based-on-conditional-3fx13piol9.pdf

Chun Shu Chen

Papers

A statistical investigation of preearthquake ionospheric anomaly

Statistical tests for pre-earthquake ionospheric anomaly

Optimal Geostatistical Model Selection

Geostatistical model averaging based on conditional information criteria

An improved Cp criterion for spline smoothing