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pro vyhledávání: '"Lunde, Robert"'
Autor:
Lunde, Robert
We study the properties of conformal prediction for network data under various sampling mechanisms that commonly arise in practice but often result in a non-representative sample of nodes. We interpret these sampling mechanisms as selection rules app
Externí odkaz:
http://arxiv.org/abs/2306.07252
An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics. While standard
Externí odkaz:
http://arxiv.org/abs/2302.10095
Publikováno v:
Neurips 2021
We consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming principal component analysis, where the data are generated IID from some unknown distribution. By combining clas
Externí odkaz:
http://arxiv.org/abs/2106.14857
We propose a new class of multiplier bootstraps for count functionals, ranging from a fast, approximate linear bootstrap tailored to sparse, massive graphs to a quadratic bootstrap procedure that offers refined accuracy for smaller, denser graphs. Fo
Externí odkaz:
http://arxiv.org/abs/2009.06170
We study the properties of a leave-node-out jackknife procedure for network data. Under the sparse graphon model, we prove an Efron-Stein-type inequality, showing that the network jackknife leads to conservative estimates of the variance (in expectat
Externí odkaz:
http://arxiv.org/abs/2004.08935
Autor:
Lunde, Robert, Sarkar, Purnamrita
We establish a general theory for subsampling network data generated by the sparse graphon model. In contrast to previous work for networks, we demonstrate validity under minimal assumptions; the main requirement is weak convergence of the functional
Externí odkaz:
http://arxiv.org/abs/1907.12528
Autor:
Lunde, Robert
We consider the problem of inference after model selection under weak assumptions in the time series setting. Even when the data are not independent, we show that sample splitting remains asymptotically valid as long as the process satisfies appropri
Externí odkaz:
http://arxiv.org/abs/1902.07425
Autor:
Lunde, Robert, Shalizi, Cosma Rohilla
We consider the problem of finding confidence intervals for the risk of forecasting the future of a stationary, ergodic stochastic process, using a model estimated from the past of the process. We show that a bootstrap procedure provides valid confid
Externí odkaz:
http://arxiv.org/abs/1711.02834
Akademický článek
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Autor:
Lunde, Robert, Sarkar, Purnamrita
Publikováno v:
Biometrika; Mar2023, Vol. 110 Issue 1, p15-32, 18p