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205
A Joint Framework for Analysis of AgriEnvironmental Payment Programs
 American Journal of Agricultural Economics
"... This article presents an approach for simultaneously estimating farmers ’ decisions to accept incentive payments in return for adopting a bundle of environmentally benign best management practices. Using the results of a multinomial probit analysis of surveys of over 1,000 farmers facing five adopti ..."
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This article presents an approach for simultaneously estimating farmers ’ decisions to accept incentive payments in return for adopting a bundle of environmentally benign best management practices. Using the results of a multinomial probit analysis of surveys of over 1,000 farmers facing five adoption decisions in a voluntary program, we show how the farmers ’ perceptions of the desirability of various bundles change with the offer amounts and with which practices are offered in the bundle. We also demonstrate an estimator for the mean minimum willingness to accept for the adoption of a practice conditional on the cost share offers for other practices. Key words: best management practices, EQIP, incentive payments, multinomial probit, simulated maximum likelihood estimation, simulated multivariate normal, WTA. Agrienvironmental payment programs play an important part in improving the environmental performance of agriculture (Claassen
What Affects the Accuracy of QuasiMonte Carlo Quadrature?
"... QuasiMonte Carlo quadrature methods have been used for several decades. Their accuracy ranges from excellent to poor, depending on the problem. This article discusses how quasiMonte Carlo quadrature error can be assessed, and what are the factors that influence it. ..."
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Cited by 14 (0 self)
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QuasiMonte Carlo quadrature methods have been used for several decades. Their accuracy ranges from excellent to poor, depending on the problem. This article discusses how quasiMonte Carlo quadrature error can be assessed, and what are the factors that influence it.
Adaptive Sampling and Forecasting With Mobile Sensor Networks
, 2009
"... This thesis addresses planning of mobile sensor networks to extract the best information possible out of the environment to improve the (ensemble) forecast at some verification region in the future. To define the information reward associated with sensing paths, the mutual information is adopted to ..."
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Cited by 14 (2 self)
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This thesis addresses planning of mobile sensor networks to extract the best information possible out of the environment to improve the (ensemble) forecast at some verification region in the future. To define the information reward associated with sensing paths, the mutual information is adopted to represent the influence of the measurement actions on the reduction of the uncertainty in the verification variables. The sensor networks planning problems are posed in both discrete and continuous time/space, each of which represents a different level of abstraction of the decision space.
Sparse Causal Discovery in Multivariate Time Series
, 2008
"... Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on nonvanishing coefficients belonging to respective timelagged instances. As in most cases a parsimonious causality structure is assumed, a promising appro ..."
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Cited by 13 (2 self)
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Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on nonvanishing coefficients belonging to respective timelagged instances. As in most cases a parsimonious causality structure is assumed, a promising approach to causal discovery consists in fitting VAR models with an additional sparsitypromoting regularization. Along this line we here propose that sparsity should be enforced for the subgroups of coefficients that belong to each pair of time series, as the absence of a causal relation requires the coefficients for all timelags to become jointly zero. Such behavior can be achieved by means of ℓ1,2norm regularized regression, for which an efficient active set solver has been proposed recently. Our method is shown to outperform standard methods in recovering simulated causality graphs. The results are on par with a second novel approach which uses multiple statistical testing.
Eigenvectors of the discrete Laplacian on regular graphs  a statistical approach
, 2008
"... In an attempt to characterize the structure of eigenvectors of random regular graphs, we investigate the correlations between the components of the eigenvectors associated to different vertices. In addition, we provide numerical observations, suggesting that the eigenvectors follow a Gaussian distri ..."
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In an attempt to characterize the structure of eigenvectors of random regular graphs, we investigate the correlations between the components of the eigenvectors associated to different vertices. In addition, we provide numerical observations, suggesting that the eigenvectors follow a Gaussian distribution. Following this assumption, we reconstruct some properties of the nodal structure which were observed in numerical simulations, but were not explained so far [1]. We also show that some statistical properties of the nodal pattern cannot be described in terms of a percolation model, as opposed to the suggested correspondence [2] for eigenvectors of 2 dimensional manifolds.
A Lego system for conditional inference
 The American Statistician
, 2006
"... Conditioning on the observed data is an important and flexible design principle for statistical test procedures. Although generally applicable, permutation tests currently in use are limited to the treatment of special cases, such as contingency tables or Ksample problems. A new theoretical framewo ..."
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Conditioning on the observed data is an important and flexible design principle for statistical test procedures. Although generally applicable, permutation tests currently in use are limited to the treatment of special cases, such as contingency tables or Ksample problems. A new theoretical framework for permutation tests opens up the way to a unified and generalized view. We argue that the transfer of such a theory to practical data analysis has important implications in many applications and requires tools that enable the data analyst to compute on the theoretical concepts as closely as possible. We reanalyze four data sets by adapting the general conceptual framework to these challenging inference problems and utilizing the coin addon package in the R system for statistical computing to show what one can gain from going beyond the ‘classical ’ test procedures.
Implementing a Class of Permutation Tests: The coin Package
"... This description of the R package coin is a (slightly) modified version of Hothorn, Hornik, van˜de Wiel, and Zeileis (2008a) published in the Journal of Statistical Software. The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nomina ..."
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This description of the R package coin is a (slightly) modified version of Hothorn, Hornik, van˜de Wiel, and Zeileis (2008a) published in the Journal of Statistical Software. The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nominal, ordered, numeric, and censored data as well as multivariate data at mixed scales. Based on a rich and flexible conceptual framework that embeds different permutation test procedures into a common theory, a computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions. As a consequence, the computational tools in coin inherit the flexibility of the underlying theory and conditional inference functions for important special cases can be set up easily. Conditional versions of classical tests—such as tests for location and scale problems in two or more samples, independence in two or threeway contingency tables, or association problems for censored, ordered categorical or multivariate data—can easily be implemented as special cases using this computational toolbox by choosing appropriate transformations of the observations. The paper gives a detailed exposition of both the internal structure of the package and the provided user interfaces along with examples on how to extend the implemented functionality. Keywords:˜conditional inference, exact distribution, conditional Monte Carlo, categorical data analysis, R. 1.
Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
"... This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the syst ..."
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This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.
Numerical Evaluation of Singular Multivariate Normal Distributions
 Journal of Statistical Computation and Simulation
, 1999
"... We present an efficient and accurate method to evaluate multivariate normal probabilities with arbitrary singular correlation matrices. The new method is applied to the construction of simultaneous confidence intervals and simultaneous all pairwise confidence intervals for multinomial proportions wh ..."
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We present an efficient and accurate method to evaluate multivariate normal probabilities with arbitrary singular correlation matrices. The new method is applied to the construction of simultaneous confidence intervals and simultaneous all pairwise confidence intervals for multinomial proportions when the sample size is sufficiently large. Key Words: multivariate normal, singular distribution, numerical integration, statistical computation. Submitted to Computational Statistics and Data Analysis 1 1 Introduction Let X 1 ; : : : ; Xm (m 2) be the standardized mvariate normal random variates with a correlation matrix fae (m) jk g. Consider the probability Pr m " j=1 ` X j b j ' ; fae (m) jk = ff jk g (1) where b 1 ; : : : ; b m 2 ! and where fae (m) jk = ff jk g denotes the correlation matrix fae (m) jk g with entry ff jk in the jth row and kth column for j 6= k and entry 1 for j = k, where 1 j; k m. The methods for evaluating the probability in (1) with ...
Alternative Sampling Methods for Estimating Multivariate Normal Probabilities
"... We study the performance of alternative sampling methods for estimating multivariate normal probabilities through the GHK simulator. The sampling methods are randomized versions of some quasiMonte Carlo samples (Halton, Niederreiter, NiederreiterXing sequences and lattice points) and some samples ..."
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We study the performance of alternative sampling methods for estimating multivariate normal probabilities through the GHK simulator. The sampling methods are randomized versions of some quasiMonte Carlo samples (Halton, Niederreiter, NiederreiterXing sequences and lattice points) and some samples based on orthogonal arrays (Latin hypercube, orthogonal array and orthogonal array based Latin hypercube samples). In general, these samples turn out to have a better performance than Monte Carlo and antithetic Monte Carlo samples. Improvements over these are large for lowdimensional (4 and 10) cases and still signi…cant for dimensions as large as 50.