48,449 research outputs found
On large-sample estimation and testing via quadratic inference functions for correlated data
Hansen (1982) proposed a class of "generalized method of moments" (GMMs) for
estimating a vector of regression parameters from a set of score functions.
Hansen established that, under certain regularity conditions, the estimator
based on the GMMs is consistent, asymptotically normal and asymptotically
efficient. In the generalized estimating equation framework, extending the
principle of the GMMs to implicitly estimate the underlying correlation
structure leads to a "quadratic inference function" (QIF) for the analysis of
correlated data. The main objectives of this research are to (1) formulate an
appropriate estimated covariance matrix for the set of extended score functions
defining the inference functions; (2) develop a unified large-sample
theoretical framework for the QIF; (3) derive a generalization of the QIF test
statistic for a general linear hypothesis problem involving correlated data
while establishing the asymptotic distribution of the test statistic under the
null and local alternative hypotheses; (4) propose an iteratively reweighted
generalized least squares algorithm for inference in the QIF framework; and (5)
investigate the effect of basis matrices, defining the set of extended score
functions, on the size and power of the QIF test through Monte Carlo simulated
experiments.Comment: 32 pages, 2 figure
Regression adjustments for estimating the global treatment effect in experiments with interference
Standard estimators of the global average treatment effect can be biased in
the presence of interference. This paper proposes regression adjustment
estimators for removing bias due to interference in Bernoulli randomized
experiments. We use a fitted model to predict the counterfactual outcomes of
global control and global treatment. Our work differs from standard regression
adjustments in that the adjustment variables are constructed from functions of
the treatment assignment vector, and that we allow the researcher to use a
collection of any functions correlated with the response, turning the problem
of detecting interference into a feature engineering problem. We characterize
the distribution of the proposed estimator in a linear model setting and
connect the results to the standard theory of regression adjustments under
SUTVA. We then propose an estimator that allows for flexible machine learning
estimators to be used for fitting a nonlinear interference functional form. We
propose conducting statistical inference via bootstrap and resampling methods,
which allow us to sidestep the complicated dependences implied by interference
and instead rely on empirical covariance structures. Such variance estimation
relies on an exogeneity assumption akin to the standard unconfoundedness
assumption invoked in observational studies. In simulation experiments, our
methods are better at debiasing estimates than existing inverse propensity
weighted estimators based on neighborhood exposure modeling. We use our method
to reanalyze an experiment concerning weather insurance adoption conducted on a
collection of villages in rural China.Comment: 38 pages, 7 figure
Bayesian Semiparametric Multivariate Density Deconvolution
We consider the problem of multivariate density deconvolution when the
interest lies in estimating the distribution of a vector-valued random variable
but precise measurements of the variable of interest are not available,
observations being contaminated with additive measurement errors. The existing
sparse literature on the problem assumes the density of the measurement errors
to be completely known. We propose robust Bayesian semiparametric multivariate
deconvolution approaches when the measurement error density is not known but
replicated proxies are available for each unobserved value of the random
vector. Additionally, we allow the variability of the measurement errors to
depend on the associated unobserved value of the vector of interest through
unknown relationships which also automatically includes the case of
multivariate multiplicative measurement errors. Basic properties of finite
mixture models, multivariate normal kernels and exchangeable priors are
exploited in many novel ways to meet the modeling and computational challenges.
Theoretical results that show the flexibility of the proposed methods are
provided. We illustrate the efficiency of the proposed methods in recovering
the true density of interest through simulation experiments. The methodology is
applied to estimate the joint consumption pattern of different dietary
components from contaminated 24 hour recalls
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An easily implemented agro-hydrological procedure with dynamic root simulation for water transfer in the crop–soil system: validation and application
Models for water transfer in the crop–soil system are key components of agro-hydrological models for irrigation, fertilizer and pesticide practices. Many of the hydrological models for water transfer in the crop–soil system are either too approximate due to oversimplified algorithms or employ complex numerical schemes. In this paper we developed a simple and sufficiently accurate algorithm which can be easily adopted in agro-hydrological models for the simulation of water dynamics. We used a dual crop coefficient approach proposed by the FAO for estimating potential evaporation and transpiration, and a dynamic model for calculating relative root length distribution on a daily basis. In a small time step of 0.001 d, we implemented algorithms separately for actual evaporation, root water uptake and soil water content redistribution by decoupling these processes. The Richards equation describing soil water movement was solved using an integration strategy over the soil layers instead of complex numerical schemes. This drastically simplified the procedures of modeling soil water and led to much shorter computer codes. The validity of the proposed model was tested against data from field experiments on two contrasting soils cropped with wheat. Good agreement was achieved between measurement and simulation of soil water content in various depths collected at intervals during crop growth. This indicates that the model is satisfactory in simulating water transfer in the crop–soil system, and therefore can reliably be adopted in agro-hydrological models. Finally we demonstrated how the developed model could be used to study the effect of changes in the environment such as lowering the groundwater table caused by the construction of a motorway on crop transpiration
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