#Kernel density estimation pdf
#Kernel density estimation software
#Kernel density estimation series

#Kernel density estimation series

We consider time series representing a wide variety of risk factors in the context of financial risk management. The recovery, publication, and availability of this catalog provide the scientific community with a valuable data set of solar characteristics that will help us to study in more detail the past solar magnetic field and long-term solar activity. For that reason, the catalog of solar observations published by the Coimbra Observatory is of enormous value. Historical observations of faculae, prominences, and filaments are not as common as sunspot records, and in addition, few historical series of these solar features are available in digital version. In addition, we show an analysis of this catalog, as well as some comparisons between solar indices calculated from Coimbra data and those from other sources. We have applied a quality control to the catalog, obtaining that the percentage of problematic or suspicious data found is lower than 1% of the total number of observations. Here we extend that work and present a digital version of the facula, prominence, and filament observations made in that observatory. In previous works, a machine-readable version on sunspot observations made in Coimbra was published. The Astronomical Observatory of the University of Coimbra (Portugal) published a catalog with solar observations such as sunspots, faculae, prominences, and filaments for the period 1929–1944. These findings could help propose targeted pandemic prevention policies applying spatial intervention to improve residents’ mental health resilience in response to future pandemics. The results reveal that (1) the characteristics of pandemic spread show contagion diffusion at the micro level and hierarchical diffusion at the macro level, (2) the pandemic has a depressive effect on public sentiment in the center of the outbreak, and (3) the pandemic has a nonlinear gradient negative impact on mood in the surrounding areas. Additionally, local Moran’s I, kernel-density analysis, Getis-Ord Gi* and standard deviation ellipse methods were applied to analyze the nonlinear evolution and clustering characteristics of public sentiment spatial-temporal patterns at the small-city scale concerning the pandemic. The natural language algorithm of dictionary-based sentiment analysis (DSA) was used to calculate public sentiment strength. To address this lack of research, we conducted a case study of Shaoxing city, proposed a spatial-based pandemic-cognition-sentiment (PCS) conceptual model, and collected microblog check-in data and information on the spatial-temporal trajectory of cases before and after a wave of the COVID-19 pandemic. However, the majority of recent studies have focused on the macro scale or large cities, and there is a relative lack of adequate research on the small-city scale in China. Therefore, it is of great value to study the spatial-temporal characteristics of public sentiment responses to COVID-19 exposure to improve urban anti-pandemic decision-making and public health resilience.

#Kernel density estimation software

We have madeĪll the code available as an open source software repository.The impact of the COVID-19 pandemic on public mental health has become increasingly prominent. Show that DMKDE is on par with its competitors for computing density estimatesĪnd advantages are shown when performed on high-dimensional data. State-of-the-art fast procedures for approximating the kernel densityĮstimation method on different synthetic data sets. Systematically evaluate the novel DMKDE algorithm and compare it with other This method has its roots in the KDE and can be considered as anĪpproximation method, without its memory-based restriction. Random Fourier features, an explicit kernel approximation, to produce densityĮstimates.

Method (DMKDE) uses density matrices, a quantum mechanical formalism, and The novel density kernel density estimation Hashing-based estimators, have been proposed to improve the efficiency of the Several strategies, such as tree-based or Uses the entire training data set for prediction, makes it unsuitable for mostĬurrent big data applications. The fact that it is a memory-based method, i.e., it

#Kernel density estimation pdf

González Download PDF Abstract: Kernel density estimation (KDE) is one of the most widely used nonparametricĭensity estimation methods.