Kernel Estimation for univariate Hawkes processes
ADAPTIVE ESTIMATION FOR HAWKES PROCESSES; APPLICATION TO GENOME ANALYSIS 1
... Hawkes process, model selection, oracle inequalities, data-driven penalty, minimax risk, adaptive estimation, unknown support, genome analysis.... model for instance.[r] ...
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Hawkes and INAR(∞) processes
... considered Hawkes process N ...nonparametric estimation procedure for the Hawkes ...a Hawkes process directly, one fits the corresponding INAR(∞) model from Theorem 2 on the bin-counts for ...
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Very fast optimal bandwidth selection for univariate kernel density estimation
... bandwidth estimation both on simulated as well as real ...the univariate case in the cur- rent paper since the bandwidth selection procedures for the univariate case are pretty ...bandwidth ...
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Gaussian process regression for the estimation of stable univariate time-series processes
... Since estimation of a finite order AR process is equivalent to the estimation of a finite impulse response, certain properties are a priori known for the AR coefficients and will be used during ...process ...
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Hawkes Processes in Finance: A Review with Simulations
... Next, estimation methods would be researched to better estimate the parameters necessary for modeling empirical ...what kernel to use, how to best tune the model, what additional attributes would be ...
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Identifying and Predicting Financial Earthquakes Using Hawkes Processes
... for Hawkes processes are ...for Hawkes models based on the Lagrange Multiplier (LM) ...a univariate model to a multivariate model, that is, we examine whether there is a conditional dependence ...
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Nonparametric drift estimation for diffusions with jumps driven by a Hawkes process
... diffusion processes seem suitable to model the membrane potential of a neuron between successive ...a Hawkes jump term, representing the influence of a network of neurons surrounding the one neuron of ...
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Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences
... triggering kernel learned by MMHP in ...triggering kernel captures the tempo- ral influence decay of a program on its following programs, which corresponds to the expected nature of user watching behaviors ...
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Modeling user and topic interactions in social networks using Hawkes processes
... linear Hawkes processes and couple the point process with a lan- guage model in order to estimate the ...coupled estimation of the language model, the influence of users and their intrinsic dif- ...
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Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences
... Multi-dimensional Hawkes processes have been proposed and applied to analyze the topic diffusion [Rodriguez et ...multi-dimensional Hawkes pro- cesses with the Infinite Relational Model (IRM) ...
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Limit theorems for nearly unstable Hawkes processes: Version with technical appendix
... of Hawkes processes. The introduction of Hawkes processes in finance is probably due to Chavez-Demoulin et ...risk estimation and to Bowsher, see [ 12 ], who jointly studied transaction ...
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Kernel density estimation on the torus
... of kernel ef- ficiency is ...of univariate functions, and therefore not much should change in higher dimen- ...the kernel have separable contributions to the mean integrated squared ...given ...
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Kernel deconvolution density estimation
... inference. Kernel deconvolution density estimation provides an alternative way to remove measurement error in estimating a target density, without the need for stringent para- metric ...Deconvolution ...
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Adaptive Estimation and Prediction of Univariate Vehicular Traffic Condition Series
... curve estimation without making strong assumptions on the true ...(K-NN), kernel smoothing, and local linear regression are three methods that have been applied to traffic ...
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logcondens: Computations Related to Univariate Log-Concave Density Estimation
... a kernel density estimate when exploring data we propose to additionally display the log-concave estimate, or its smoothed version described below, to get an idea about the distribution of the ...sizes, ...
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Kernel Density Estimation for Heaped Data
... Density Estimation for Heaped Data Marcus Groß, Ulrich Rendtel ∗ Abstract In self-reported data usually a phenomenon called ‘heaping’ occurs, ...applying kernel density methods naively to the rounded ...in ...
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Processes and the Kernel
... EX_CPU 11 /* Coprocessor unusable */ EX_OVF 12 /* Arithmetic overflow */ On the MIPS, the same mechanism handles exceptions and inter- rupts, and there is a single handler for both in the kernel. The handler uses ...
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Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks
... unrelated processes. Meanwhile, we can see that the Hawkes process based mod- els, such as our model, Coevol, and LRH, get better perfor- mances when there are sufficient history events (with nearly 400 ...
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Some Bayesian methods for univariate density estimation
... Even though the histogram is the oldest density estimator according to Scott, D., Tapia, R., and Thompson, J. (1977) and (Silverman, B. , 1998, p. 7), it is also among the most extensively applied (Wegman, E. , 1972; ...
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On the Estimation of a Univariate Gaussian Distribution: A Comparative Approach
... Abstract Estimation of the unknown mean, μ and variance, σ 2 of a univariate Gaussian distribution ( ) N µ σ , 2 given a single study variable x is ...likelihood estimation (MLE), and the method of ...
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