Resampling methods for spatial regression models under a class of stochastic designs

In this paper we consider the problem of bootstrapping a class of spatial regression models when the sampling sites are generated by a (possibly nonuniform) stochastic design and are irregularly spaced. It is shown that the natural extension of the existing block bootstrap methods for grid spatial data does not work for irregularly spaced spatial data under nonuniform stochastic designs. A variant of the blocking mechanism is proposed. It is shown that the proposed block bootstrap method provides a valid approximation to the distribution of a class of M-estimators of the spatial regression parameters. Finite sample properties of the method are investigated through a moderately large simulation study and a real data example is given to illustrate the methodology.Comment: Published at http://dx.doi.org/10.1214/009053606000000551 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Oil Blending: Mixing and Contamination

The Shell Company of Australia has a frequent need to blend lubricants. Blending, sometimes involving three lubricant oils and additives, takes place by jet mixing in large tanks of typically 45,000 titres capacity. The jets are driven by pumps with typical volume throughput of up to 1,000 titres per minute, and typical blending times may be as long as one or two hours. The jet blending process was investigated in a number of ways at the Study Group. These included: simple estimates for blending times, theoretical and experimental description of jet behaviour, development of a simple compartment model for the blending process, and several large scale computer simulations of the jet-induced motion using a commercial Computational Fluid Dynamics package. In addition, the sedimentation of contaminant particles in the tanks was investigated. This overall investigation, using a variety of approaches, gave a good knowledge of the blending process

Numerical Study of Quantum Hall Bilayers at Total Filling νT=1\nu_T=1: A New Phase at Intermediate Layer Distances

We study the phase diagram of quantum Hall bilayer systems with total filing $\nu_T=1/2+1/2$ of the lowest Landau level as a function of layer distances $d$. Based on numerical exact diagonalization calculations, we obtain three distinct phases, including an exciton superfluid phase with spontaneous interlayer coherence at small $d$, a composite Fermi liquid at large $d$, and an intermediate phase for $1.1<d/l_B<1.8$ ($l_B$ is the magnetic length). The transition from the exciton superfluid to the intermediate phase is identified by (i) a dramatic change in the Berry curvature of the ground state under twisted boundary conditions on the two layers; (ii) an energy level crossing of the first excited state. The transition from the intermediate phase to the composite Fermi liquid is identified by the vanishing of the exciton superfluid stiffness. Furthermore, from our finite-size study, the energy cost of transferring one electron between the layers shows an even-odd effect and possibly extrapolates to a finite value in the thermodynamic limit, indicating the enhanced intralayer correlation. Our identification of an intermediate phase and its distinctive features shed new light on the theoretical understanding of the quantum Hall bilayer system at total filling $\nu_T=1$.Comment: 5 pages, 3 figures (main text); 5 pages, 4 figures (supplementary material); to be published in PR

The Bayesian sampler : generic Bayesian inference causes incoherence in human probability

Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of “noise” in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017; Costello & Watts, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities and distributions of probability estimates. We show in 2 new experiments that this model better captures these mean judgments both qualitatively and quantitatively; which model best fits individual distributions of responses depends on the assumed size of the cognitive sample

Rain or Shine: Where is the Weather Effect?

Saunders (1993) and Hirshleifer and Shumway (2001) document the effect of weather on stock returns. The proposed explanation in both papers is that investor mood affects cognitive processes and trading decisions. In this paper, we use a database of individual investor accounts to examine the weather effects on traders. Our analysis of the trading activity in five major U.S. cities over a six-year period finds vistually no difference in individuals propensity to buy or sell equities on cloudy days as opposed to sunny days. If the association between cloud cover and stock returns documented for New York and other world cities is indeed caused by investor mood swings, our findings suggest that researchers should focus on the attitudes of market-makers, news providers or other agents physically located in the city hosting the exchange. NYSE spreads widen on cloudy days. When we control for this, the significance of the weather effect is dramatically reduced. We interpret this as evidence that the behavior of market-makers, rather than individual investors, may be responsible for the relation between returns and weather.