Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
120
result(s) for
"Stroud, Jonathan"
Sort by:
The creeping shadow
\"A terrible crime forces Lockwood to turn to Lucy for help, setting them on the trail of dark secrets at the heart of London society. Both professionally and personally, their investigation stirs up forces they may not be able to control. . . \"-- Provided by publisher.
Book
Understanding the Ensemble Kalman Filter
The ensemble Kalman filter (EnKF) is a computational technique for approximate inference in state-space models. In typical applications, the state vectors are large spatial fields that are observed sequentially over time. The EnKF approximates the Kalman filter by representing the distribution of the state with an ensemble of draws from that distribution. The ensemble members are updated based on newly available data by shifting instead of reweighting, which allows the EnKF to avoid the degeneracy problems of reweighting-based algorithms. Taken together, the ensemble representation and shifting-based updates make the EnKF computationally feasible even for extremely high-dimensional state spaces. The EnKF is successfully used in data-assimilation applications with tens of millions of dimensions. While it implicitly assumes a linear Gaussian state-space model, it has also turned out to be remarkably robust to deviations from these assumptions in many applications. Despite its successes, the EnKF is largely unknown in the statistics community. We aim to change that with the present article, and to entice more statisticians to work on this topic.
Journal Article
The whispering skull
\"Lockwood & Co. are hired to investigate Edmund Bickerstaff, a Victorian doctor who reportedly tried to communicate with the dead, while Lucy is distracted by urgent whispers coming from the skull in a ghost jar\"-- Provided by publisher.
Book
Bayesian and Maximum Likelihood Estimation for Gaussian Processes on an Incomplete Lattice
This article proposes a new approach for Bayesian and maximum likelihood parameter estimation for stationary Gaussian processes observed on a large lattice with missing values. We propose a Markov chain Monte Carlo approach for Bayesian inference, and a Monte Carlo expectation-maximization algorithm for maximum likelihood inference. Our approach uses data augmentation and circulant embedding of the covariance matrix, and provides likelihood-based inference for the parameters and the missing data. Using simulated data and an application to satellite sea surface temperatures in the Pacific Ocean, we show that our method provides accurate inference on lattices of sizes up to 512 × 512, and is competitive with two popular methods: composite likelihood and spectral approximations.
Journal Article
The empty grave
\"With the help of some unexpected and rather ghostly allies, Lockwood & Co. must battle their greatest enemy yet, as they move ever closer to the moment when the earth-shattering secret of 'the problem' will finally be revealed\"-- Provided by publisher.
Book
Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices
This paper provides an optimal filtering methodology in discretely observed continuoustime jump-diffusion models. Although the filtering problem has received little attention, it is useful for estimating latent states, forecasting volatility and returns, computing model diagnostics such as likelihood ratios, and parameter estimation. Our approach combines time-discretization schemes with Monte Carlo methods. It is quite general, applying in nonlinear and multivariate jump-diffusion models and models with nonanalytic observation equations. We provide a detailed analysis of the filter's performance, and analyze four applications: disentangling jumps from stochastic volatility, forecasting volatility, comparing models via likelihood ratios, and filtering using option prices and returns.
Journal Article
Bayesian Modeling and Forecasting of 24-Hour High-Frequency Volatility
This article estimates models of high-frequency index futures returns using \"around-the-clock\" 5-min returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns, correlations between return and volatility shocks, and announcement effects. We develop an integrated MCMC approach to estimate interday and intraday parameters and states using high-frequency data without resorting to various aggregation measures like realized volatility. We provide a case study using financial crisis data from 2007 to 2009, and use particle filters to construct likelihood functions for model comparison and out-of-sample forecasting from 2009 to 2012. We show that our approach improves realized volatility forecasts by up to 50% over existing benchmarks and is also useful for risk management and trading applications. Supplementary materials for this article are available online.
Journal Article
A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation
This paper proposes new methodology for sequential state and parameter estimation within the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior distribution of states and parameters over time. To implement the method, the authors consider three representations of the marginal posterior distribution of the parameters: a grid-based approach, a Gaussian approximation, and a sequential importance sampling (SIR) approach with kernel resampling. In contrast to existing online parameter estimation algorithms, the new method explicitly accounts for parameter uncertainty and provides a formal way to combine information about the parameters from data at different time periods. The method is illustrated and compared to existing approaches using simulated and real data.
Journal Article
Bayesian Forecasting of an Inhomogeneous Poisson Process With Applications to Call Center Data
A call center is a centralized hub where customer and other telephone calls are handled by an organization. In today's economy, call centers have become the primary points of contact between customers and businesses. Thus accurate predictions of call arrival rates are indispensable to help call center practitioners staff their call centers efficiently and cost-effectively. This article proposes a multiplicative model for modeling and forecasting within-day arrival rates to a U.S. commercial bank's call center. Markov chain Monte Carlo sampling methods are used to estimate both latent states and model parameters. One-day-ahead density forecasts for the rates and counts are provided. The calibration of these predictive distributions is evaluated through probability integral transforms. Furthermore, 1-day-ahead forecasts comparisons with classical statistical models are given. Our predictions show significant improvements of up to 25% over these standards. A sequential Monte Carlo algorithm is also proposed for sequential estimation and forecasts of the model parameters and rates.
Journal Article
A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning about Return Predictability
We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.
Journal Article