论文检索

双重稳健因果推断的贝叶斯视角：表1AI 解读

In causal inference the effect of confounding may be controlled using regression adjustment in an outcome model, propensity score adjustment, inverse probability of treatment weighting or a combination of these. Approaches based on modelling the treatment assignment mechanism, along with their doubly robust extensions, have been difficult to motivate using formal likelihood-based or Bayesian arguments, as the treatment assignment model plays no part in inferences concerning…

基于种群的可逆跳跃马尔可夫链蒙特卡洛AI 解读

In this paper we present an extension of population-based Markov chain Monte Carlo (MCMC) to the trans-dimensional case. One of the main challenges in MCMC-based inference is that of simulating from high and trans-dimensional target measures. In such cases, MCMC methods may not adequately traverse the support of the target; the simulation results will be unreliable. We develop population methods to deal with such problems, and give a result proving the uniform ergodicity of…

Lévy 过程驱动的随机波动率模型：模拟与推断AI 解读

We study Ornstein-Uhlenbeck stochastic processes driven by Lévy processes, and extend them to more general non-Ornstein-Uhlenbeck models. In particular, we investigate the means of making the correlation structure in the volatility process more flexible. For one model, we implement a method for introducing quasi long-memory into the volatility model. We demonstrate that the models can be fitted to real share price returns data.

季节性持久过程周期图估计量的大样本性质AI 解读

Seasonally persistent models were first introduced by Anděl (1986) and Gray et al. (1989) to extend autoregressive moving‐average and fractionally differenced models and to encompass long‐memory quasi‐periodic behaviour. These models are, for certain ranges of parameters, stationary, and we prove here that the behaviour of the periodogram and other tapered estimators cannot be simply extended from the work of Künsch (1986) and Hurvich & Beltrao (1993) on long memory induced…