Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Kuffner, Todd A."'
In this paper, we investigate the theoretical properties of stochastic gradient descent (SGD) for statistical inference in the context of nonconvex optimization problems, which have been relatively unexplored compared to convex settings. Our study is
Externí odkaz:
http://arxiv.org/abs/2306.02205
We propose a new method named the Conditional Randomization Rank Test (CRRT) for testing conditional independence of a response variable Y and a covariate variable X, conditional on the rest of the covariates Z. The new method generalizes the Conditi
Externí odkaz:
http://arxiv.org/abs/2112.00258
Publikováno v:
In Journal of Econometrics February 2024 239(1)
Volatility estimation based on high-frequency data is key to accurately measure and control the risk of financial assets. A L\'{e}vy process with infinite jump activity and microstructure noise is considered one of the simplest, yet accurate enough,
Externí odkaz:
http://arxiv.org/abs/1909.04853
Accurate approximation of the sampling distribution of nonparametric kernel density estimators is crucial for many statistical inference problems. Since these estimators have complex asymptotic distributions, bootstrap methods are often used for this
Externí odkaz:
http://arxiv.org/abs/1909.02662
Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. agricultural yield, along with a higher-frequenc
Externí odkaz:
http://arxiv.org/abs/1808.07861
In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected sub-model, may not be valid because i
Externí odkaz:
http://arxiv.org/abs/1712.02379
We establish a general theory of optimality for block bootstrap distribution estimation for sample quantiles under a mild strong mixing assumption. In contrast to existing results, we study the block bootstrap for varying numbers of blocks. This corr
Externí odkaz:
http://arxiv.org/abs/1710.02537
Autor:
Kolassa, John E., Kuffner, Todd A.
We consider a fundamental open problem in parametric Bayesian theory, namely the validity of the formal Edgeworth expansion of the posterior density. While the study of valid asymptotic expansions for posterior distributions constitutes a rich litera
Externí odkaz:
http://arxiv.org/abs/1710.01871
Autor:
Kolassa, John E., Kuffner, Todd A.
Publikováno v:
The Annals of Statistics, 2020 Aug 01. 48(4), 1940-1958.
Externí odkaz:
https://www.jstor.org/stable/26931544