5,815 research outputs found
Distributed Inference over Linear Models using Alternating Gaussian Belief Propagation
We consider the problem of maximum likelihood estimation in linear models
represented by factor graphs and solved via the Gaussian belief propagation
algorithm. Motivated by massive internet of things (IoT) networks and edge
computing, we set the above problem in a clustered scenario, where the factor
graph is divided into clusters and assigned for processing in a distributed
fashion across a number of edge computing nodes. For these scenarios, we show
that an alternating Gaussian belief propagation (AGBP) algorithm that
alternates between inter- and intra-cluster iterations, demonstrates superior
performance in terms of convergence properties compared to the existing
solutions in the literature. We present a comprehensive framework and introduce
appropriate metrics to analyse AGBP algorithm across a wide range of linear
models characterised by symmetric and non-symmetric, square, and rectangular
matrices. We extend the analysis to the case of dynamic linear models by
introducing dynamic arrival of new data over time. Using a combination of
analytical and extensive numerical results, we show the efficiency and
scalability of AGBP algorithm, making it a suitable solution for large-scale
inference in massive IoT networks.Comment: 14 pages, 18 figure
Does Hazardous Waste Matter? Evidence from the Housing Market and the Superfund Program
Approximately ) has been spent on Superfund clean-ups of hazardous waste sites, and remediation efforts are incomplete at roughly half of the 1,500 Superfund sites. This study estimates the effect of Superfund clean-ups on local housing price appreciation. We compare housing price growth in the areas surrounding the first 400 hazardous waste sites to be cleaned up through the Superfund program to the areas surrounding the 290 sites that narrowly missed qualifying for these clean-ups. We cannot reject that the clean-ups had no effect on local housing price growth, nearly two decades after these sites became eligible for them. This finding is robust to a series of specification checks, including the application of a quasi-experimental regression discontinuity design based on knowledge of the selection rule. Overall, the preferred estimates suggest that the benefits of Superfund clean-ups as measured through the housing market are substantially lower than the $43 million mean cost of Superfund clean-ups.Valuation of environmental goods, Hazardous waste sites, Environmental regulation, Regression discontinuity, Superfound, Externalities
Monetary Valuation of Waterfront Open Space in Coastal Areas of Mississippi and Alabama
Open space provides a wide range of ecosystem services to communities. In growing communities, open space offers relief from congestion and other negative externalities associated with rapid development. To make effective policy and planning decisions pertaining to open space preservation, it is important to estimate monetary values of its benefits. In addition, assessing public opinions regarding open space provides information on demand and how residents value open space. This study estimated the monetary value of open space in Mississippi and Alabama Gulf Coast communities. The study also collected information on coastal residentsâ attitudes towards open space, working waterfronts, and their willingness to support waterfront open space preservation monetarily. Two methodological approaches were employed to estimate the monetary value of waterfront open space: contingent valuation (CVM) and hedonic price (HPM) methods. Data were collected using a mail survey, the Multiple Listing Service (MLS), and publicly available data sources such as the U.S. Census. Data were analyzed using an interval regression, ordinary least squares, and geographically weighted regression (GWR) models. Mail survey results indicated that the majority of residents valued open space and were willing to pay from 162.14 per household as estimated by four different interval-censored econometric models. Respondentâs membership in groups promoting conservation goals, income, age, and residence duration were major factors associated with their willingness to pay. Results from the HPM indicated proximities to waterfronts, with the exception of bayous, were positively related to home prices, suggesting open space produced positive economic benefits. Findings from the HPM analysis using publicly available data were consistent and comparable with the results from the HPM that used MLS data. This similarity of results indicates the use of publicly available data is feasible in HPM analysis, which is important for broad applications of the method during city planning. In addition, GWR estimates provided site specific monetary values of waterfront open space benefits, which will be helpful for policymakers and city planners in developing site-specific conservation and preservation strategies. Findings can help formulate future decisions related to alternative development scenarios of coastal areas and conservation efforts to preserve open space
Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments
We propose strategies to estimate and make inference on key features of
heterogeneous effects in randomized experiments. These key features include
best linear predictors of the effects using machine learning proxies, average
effects sorted by impact groups, and average characteristics of most and least
impacted units. The approach is valid in high dimensional settings, where the
effects are proxied by machine learning methods. We post-process these proxies
into the estimates of the key features. Our approach is generic, it can be used
in conjunction with penalized methods, deep and shallow neural networks,
canonical and new random forests, boosted trees, and ensemble methods. It does
not rely on strong assumptions. In particular, we don't require conditions for
consistency of the machine learning methods. Estimation and inference relies on
repeated data splitting to avoid overfitting and achieve validity. For
inference, we take medians of p-values and medians of confidence intervals,
resulting from many different data splits, and then adjust their nominal level
to guarantee uniform validity. This variational inference method is shown to be
uniformly valid and quantifies the uncertainty coming from both parameter
estimation and data splitting. We illustrate the use of the approach with two
randomized experiments in development on the effects of microcredit and nudges
to stimulate immunization demand.Comment: 53 pages, 6 figures, 15 table
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