2,712 research outputs found

    An autoregressive approach to house price modeling

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    A statistical model for predicting individual house prices and constructing a house price index is proposed utilizing information regarding sale price, time of sale and location (ZIP code). This model is composed of a fixed time effect and a random ZIP (postal) code effect combined with an autoregressive component. The former two components are applied to all home sales, while the latter is applied only to homes sold repeatedly. The time effect can be converted into a house price index. To evaluate the proposed model and the resulting index, single-family home sales for twenty US metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P/Case--Shiller model, which is a repeat sales model, and a conventional mixed effects model. Finally, Los Angeles, CA, is used to illustrate a historical housing market downturn.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS380 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Minimax Linear Estimation in a White Noise Problem

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    Linear estimation of f(x) at a point in a white noise model is considered. The exact linear minimax estimator of f(0) is found for the family of f(x) in which f′(x) is Lip (M). The resulting estimator is then used to verify a conjecture of Sacks and Ylvisaker concerning the near optimality of the Epanechnikov kernel

    Bayesian Aspects of Some Nonparametric Problems

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    We study the Bayesian approach to nonparametric function estimation problems such as nonparametric regression and signal estimation. We consider the asymptotic properties of Bayes procedures for conjugate (= Gaussian) priors. We show that so long as the prior puts nonzero measure on the very large parameter set of interest then the Bayes estimators are not satisfactory. More specifically, we show that these estimators do not achieve the correct minimax rate over norm bounded sets in the parameter space. Thus all Bayes estimators for proper Gaussian priors have zero asymptotic efficiency in this minimax sense. We then present a class of priors whose Bayes procedures attain the optimal minimax rate of convergence. These priors may be viewed as compound, or hierarchical, mixtures of suitable Gaussian distributions

    Bayesian Nonparametric Point Estimation Under a Conjugate Prior

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    Estimation of a nonparametric regression function at a point is considered. The function is assumed to lie in a Sobolev space, Sq, of order q. The asymptotic squared-error performance of Bayes estimators corresponding to Gaussian priors is investigated as the sample size, n, increases. It is shown that for any such fixed prior on Sq the Bayes procedures do not attain the optimal minimax rate over balls in Sq. This result complements that in Zhao (Ann. Statist. 28 (2000) 532) for estimating the entire regression function, but the proof is rather different

    Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding

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    Large language models (LLMs) have made significant progress in various domains, including healthcare. However, the specialized nature of clinical language understanding tasks presents unique challenges and limitations that warrant further investigation. In this study, we conduct a comprehensive evaluation of state-of-the-art LLMs, namely GPT-3.5, GPT-4, and Bard, within the realm of clinical language understanding tasks. These tasks span a diverse range, including named entity recognition, relation extraction, natural language inference, semantic textual similarity, document classification, and question-answering. We also introduce a novel prompting strategy, self-questioning prompting (SQP), tailored to enhance LLMs' performance by eliciting informative questions and answers pertinent to the clinical scenarios at hand. Our evaluation underscores the significance of task-specific learning strategies and prompting techniques for improving LLMs' effectiveness in healthcare-related tasks. Additionally, our in-depth error analysis on the challenging relation extraction task offers valuable insights into error distribution and potential avenues for improvement using SQP. Our study sheds light on the practical implications of employing LLMs in the specialized domain of healthcare, serving as a foundation for future research and the development of potential applications in healthcare settings.Comment: 19 pages, preprin

    Assessment of Past and Present Sediment Quality of Stoney Creek in Burnaby, British Columbia

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    In analyzing the sediment and water quality of the Stoney Creek habitat, four key aspects were investigated: lithology, sediment/water quality, salmon spawning/incubation, and particle size distribution. The lithology found the streambed sediment layer is 3 cm in depth (over bedrock) and consists mainly of sand and some coarser material including gravels, cobbles, and boulders. The sediment of the offchannel pond is mainly mud (fine material) with a moderate amount of sand and a very small percentage of coarser material including gravels and organic matter (leaf detritus and woody debris). Chemical analysis concluded a significant concentration of iron in the pond environment, with potential for adverse effects to salmon offspring. This report further aims to assess the influences of fine sediment on the quality of salmon spawning habitat and incubation success rate. Permeability of spawning gravels and dissolved oxygen concentrations are measured to see if they support the incubation and growth of salmon eggs. Particle size distributions are found significantly different between upstream pool and pond side. And the difference of particle size distributions can influence salmon production in the off-channel site