Accurately detecting Parkinson's disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. Towards this end, the premotor stage in PD should be carefully monitored. An innovative deep-learning technique is introduced to early uncover whether an individual is affected with PD or not based on premotor features. Specifically, to uncover PD at an early stage, several indicators have been considered in this study, including Rapid Eye Movement and olfactory loss, Cerebrospinal fluid data, and dopaminergic imaging markers. A comparison between the proposed deep learning model and twelve machine learning and ensemble learning methods based on relatively small data including 183 healthy individuals and 401 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 96.45% on average. Besides detecting the PD, we also provide the feature importance on the PD detection process based on the Boosting method.

Popular approaches to spatial cluster detection, such as the spatial scan statistic, are defined in terms of the responses. Here, we consider a varying-coefficient regression and spatial clusters in the regression coefficients. For varying-coefficient regression, such as the geographically weighted regression, different regression coefficients are obtained for different spatial units. It is often of interest to the practitioners to identify clusters of spatial units with distinct patterns in a regression coefficient, but there is no formal statistical methodology for that. Rather, cluster identification is often ad-hoc such as by eyeballing the map of fitted regression coefficients and discerning patterns. In this paper, we develop new methodology for spatial cluster detection in the regression setting based on hypotheses testing. We evaluate our methods in terms of power and coverages for true clusters via simulation studies. For illustration, our methodology is applied to a cancer mortality dataset.

Wind power is one of the most potential energies and the major available renewable energy sources. Precisely predicting wind power production is essential for the management and the integration of wind power in a smart grid. The goal of this study is to predict wind power production with sufficient accuracy based on various factors using ensemble learning-based methods that consider the time-dependent nature of the wind power measurements. Essentially, the ensemble learning methods combine multiple learners to obtain an enhanced prediction performance in comparison to conventional standalone learners. In addition, they reduce the overall prediction error and have the capacity to merge various models. At first, this paper investigates the prediction capability of the well-known ensemble approaches Boosted Trees, Random Forest, and Generalized Random Forest for wind power prediction. We compared the prediction performance of these ensemble models to two frequently used prediction methods: Gaussian process regression, and Support Vector Regression. Experimental measurements recorded every ten minutes from actual wind turbines located in France and Turkey are used to test the prediction efficiency of the studied models. Experimental results have shown that the ensemble methods can predict wind power production with high accuracy compared to the standalone models. Furthermore, the findings clearly reveal that the lagged variables contribute significantly to the ensemble models, and permits constructing more parsimonious models.

No abstract

Identifying spatial clusters of different regression coefficients is a useful tool for discerning the distinctive relationship between a response and covariates in space. Most of the existing cluster detection methods aim to identify the spatial similarity in responses, and the standard cluster detection algorithm assumes independent spatial units. However, the response variables are spatially correlated in many environmental applications. We propose a mixed‐effects model for spatial cluster detection that takes spatial correlation into account. Compared to a fixed‐effects model, the introduced random effects explain extra variability among the spatial responses beyond the cluster effect, thus reducing the false positive rate. The developed method exploits a sequential searching scheme and is able to identify multiple potentially overlapping clusters. We use simulation studies to evaluate the performance of our proposed method in terms of the true and false positive rates of a known cluster and the identification of multiple known clusters. We apply our proposed methodology to particulate matter (PM2.5) concentration data from the Northeastern United States in order to study the weather effect on PM2.5 and to investigate the association between the simulations from a numerical model and the satellite‐derived aerosol optical depth data. We find geographical hot spots that show distinct features, comparing to the background.