搜索结果: 1-15 共查到“数理统计学 Processes”相关记录17条 . 查询时间(0.236 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：A Sparse Expansion of (Deep) Gaussian Processes
深层 高斯过程 稀疏展开
2023/4/18
Conventional inferential methods for (deep) Gaussian Processes models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inferen...
A Strong Law of Large Numbers for Super-stable Processes
Super-stable process Super-Brownian motion Strong law of large Preprint submitted to Annals of Probability
2016/1/25
A Strong Law of Large Numbers for Super-stable Processes.
Law of large numbers for branching symmetric Hunt processes with measure-valued branching rates
Law of large numbers branching Hunt processes spine approach h-transform spectral gap
2016/1/20
We establish weak and strong law of large numbers for a class of branching symmetric Hunt processes with the branching rate being a smooth measure with respect to the underlying Hunt process, and the ...
On large deviations in testing simple hypotheses for locally stationary Gaussian processes
Hypothesis testing Likelihood ratio Large deviations Locally stationary Gaussian processes Hoeffding bound Stein’s lemma Chernoff bound
2015/8/25
We derive a large deviation result for the log-likelihood ratio for testing simple hypotheses in locally stationary Gaussian processes. This result allows us to find explicitly the rates of exponentia...
Scaling Multidimensional Inference for Structured Gaussian Processes
Gaussian Processes Backfitting Projection Pursuit-Reression Kronecker matrices
2012/11/22
Exact Gaussian Process (GP) regression has O(N^3) runtime for data size N, making it intractable for large N. Many algorithms for improving GP scaling approximate the covariance with lower rank matric...
We propose a multiresolution Gaussian process to capture long-range, non-Markovian dependencies while allowing for abrupt changes. The multiresolution GP hierarchically couples a collection of smooth ...
Modeling high-frequency financial data by pure jump processes
Diffusion pure jump process semi-martingales high-frequency data hypothesis testing
2012/6/21
It is generally accepted that the asset price processes contain jumps. In fact, pure jump models have been widely used to model asset prices and/or stochastic volatilities. The question is: is there a...
Quantization of long memory processes
Long-memory processes Round-off error Measurement error Log-periodogram regression Detrended fluctuation analysis Hermite polynomials
2011/10/9
Abstract: We study how quantization, occurring when a continuously varying process is approximated by or observed on a grid of discrete values, changes the properties of a Gaussian long-memory process...
High-frequency sampling and kernel estimation for continuous-time moving average processes
CARMA process continuous-time moving average process discretely sampled process FICARMA process gamma kernel
2011/9/19
Abstract: Interest in continuous-time processes has increased rapidly in recent years, largely because of the high-frequency data available in many areas of application, particularly in finance and tu...
Asymptotics for minimisers of convex processes
argmin lemma approximation convexity Cox regression LAD regression log-concavity logistic regression
2011/9/15
Abstract: By means of two simple convexity arguments we are able to develop a general method for proving consistency and asymptotic normality of estimators that are defined by minimisation of convex c...
Expectiles for subordinated Gaussian processes with applications
expectiles robustness local shift sensitivity subordinated Gaussian process fractional Brownian motion
2011/8/24
Abstract: In this paper, we introduce a new class of estimators of the Hurst exponent of the fractional Brownian motion (fBm) process. These estimators are based on sample expectiles of discrete varia...
Limit theorems for bifurcating autoregressive processes with missing data
Limit theorems bifurcating autoregressive processes missing data
2011/1/4
We study the asymptotic behavior of the least squares estimators of the unknown parameters of bifurcating autoregressive processes when some of the data are missing. We model the process of observed d...
Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes
Long range dependence linear processes wavelet estimator semiparametric estimator
2011/1/18
This paper is first devoted to study an adaptive wavelet based estimator of the long memory parameter for linear processes in a general semi-parametric frame. This is an extension of Bardet et al. (20...
Marginal density estimation for linear processes with seasonal long memory
Marginal density estimation linear processes seasonal long memory
2011/1/4
Some convergence results on the kernel density estimator are proven for a class of linear processes with seasonal effects. In particular we extend the results of Ho and Hsing (1996a) and Mielniczuk (1...
Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes
Scalable Inference Customer Similarities Interactions Data Dirichlet Processes
2011/1/4
Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from...