Search results for "Kernel smoother"
showing 6 items of 6 documents
Probabilistic Forecast for Northern New Zealand Seismic Process Based on a Forward Predictive Kernel Estimator
2011
In seismology predictive properties of the estimated intensity function are often pursued. For this purpose, we propose an estimation procedure in time, longitude, latitude and depth domains, based on the subsequent increments of likelihood obtained adding an observation one at a time. On the basis of this estimation approach a forecast of earthquakes of a given area of Northern New Zealand is provided, assuming that future earthquakes activity may be based on the smoothing of past earthquakes.
Optimized Kernel Entropy Components
2016
This work addresses two main issues of the standard Kernel Entropy Component Analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis. In this work, we propose an extension of the KECA method, named Optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular…
An Introduction to Kernel Methods
2009
Machine learning has experienced a great advance in the eighties and nineties due to the active research in artificial neural networks and adaptive systems. These tools have demonstrated good results in many real applications, since neither a priori knowledge about the distribution of the available data nor the relationships among the independent variables should be necessarily assumed. Overfitting due to reduced training data sets is controlled by means of a regularized functional which minimizes the complexity of the machine. Working with high dimensional input spaces is no longer a problem thanks to the use of kernel methods. Such methods also provide us with new ways to interpret the cl…
A comparison of nonparametric methods in the graduation of mortality: Application to data from the Valencia Region (Spain)
2006
[EN] The nonparametric graduation of mortality data aims to estimate death rates by carrying out a smoothing of the crude rates obtained directly from original data. The main difference with regard to parametric models is that the assumption of an age-dependent function is unnecessary, which is advantageous when the information behind the model is unknown, as one cause of error is often the choice of an inappropriate model. This paper reviews the various alternatives and presents their application to mortality data from the Valencia Region, Spain. The comparison leads us to the conclusion that the best model is a smoothing by means of Generalised Additive Models (GAM) with splines. The most…
Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved
2019
International audience; Millions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, k…
Proportional Small Sample Bias in Pricing Kernel Estimations
2014
Numerous empirical studies find pricing kernels that are not-monotonically decreasing; the findings are at odds with the pricing kernel being marginal utility of a risk-averse, so-called representative agent. We study in detail the common procedure which estimates the pricing kernel as the ratio of two separate density estimations. In a first step, we analyze theoretically the functional dependence for the ratio of a density to its estimated density; this cautions the reader of potential computational issues coupled with statistical techniques. In a second step, we study this quantitatively; we show that small sample biases shape the estimated pricing kernel, and that estimated pricing kern…