68 research outputs found
Endogenous post-stratification in surveys: classifying with a sample-fitted model
Post-stratification is frequently used to improve the precision of survey
estimators when categorical auxiliary information is available from sources
outside the survey. In natural resource surveys, such information is often
obtained from remote sensing data, classified into categories and displayed as
pixel-based maps. These maps may be constructed based on classification models
fitted to the sample data. Post-stratification of the sample data based on
categories derived from the sample data (``endogenous post-stratification'')
violates the standard post-stratification assumptions that observations are
classified without error into post-strata, and post-stratum population counts
are known. Properties of the endogenous post-stratification estimator are
derived for the case of a sample-fitted generalized linear model, from which
the post-strata are constructed by dividing the range of the model predictions
into predetermined intervals. Design consistency of the endogenous
post-stratification estimator is established under mild conditions. Under a
superpopulation model, consistency and asymptotic normality of the endogenous
post-stratification estimator are established, showing that it has the same
asymptotic variance as the traditional post-stratified estimator with fixed
strata. Simulation experiments demonstrate that the practical effect of first
fitting a model to the survey data before post-stratifying is small, even for
relatively small sample sizes.Comment: Published in at http://dx.doi.org/10.1214/009053607000000703 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Rank-based estimation for all-pass time series models
An autoregressive-moving average model in which all roots of the
autoregressive polynomial are reciprocals of roots of the moving average
polynomial and vice versa is called an all-pass time series model. All-pass
models are useful for identifying and modeling noncausal and noninvertible
autoregressive-moving average processes. We establish asymptotic normality and
consistency for rank-based estimators of all-pass model parameters. The
estimators are obtained by minimizing the rank-based residual dispersion
function given by Jaeckel [Ann. Math. Statist. 43 (1972) 1449--1458]. These
estimators can have the same asymptotic efficiency as maximum likelihood
estimators and are robust. The behavior of the estimators for finite samples is
studied via simulation and rank estimation is used in the deconvolution of a
simulated water gun seismogram.Comment: Published at http://dx.doi.org/10.1214/009053606000001316 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The detection and estimation of long memory in stochastic volatility
We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of the model are obtained by maximizing the spectral approximation to the Gaussian likelihood. The finite sample properties of the spectral likelihood estimator are analyzed by means of a Monte Carlo study. An empirical example with a long time series of stock prices demonstrates the superiority of the LMSV model over existing (short-memory) volatility models.info:eu-repo/semantics/publishedVersio
Optimal Information Acquisition Under a Geostatistical Model
Studies examining the value of switching to a variable rate technology (VRT) fertilizer program assume producers possess perfect soil nitrate information. In reality, producers estimate soil nitrate levels with soil sampling. The value of switching to a VRT program depends on the quality of the estimates and on how the estimates are used. Larger sample sizes, increased spatial correlation, and decreased variability improve the estimates and increase returns. Fertilizing strictly to the estimated field map fails to account for estimation risk. Returns increase if the soil sample information is used in a Bayesian fashion to update the soil nitrate beliefs in nonsampled sites
Sampling Schemes for Policy Analyses Using Computer Simulation Experiments
Evaluating the environmental and Economic impacts of agricultural policies is not a simple task. A systematic approach to evaluation would include the effect of policy-dependent factors (such as tillage practices, crop rotations, and chemical use) as well as the effect of policy independent covariates (such as weather, topography, and soil attributes) on response variables (such as amount of soil eroded or chemical leached into the groundwater). For comparison purposes, the effects of these input combinations on the response variable would have to be assessed under competing policy scenarios. Because the number of input combinations is high in most problems, and because policies to be evaluated are often not in use at the time of the study, practitioners have resorted to simulation experiments to generate data. But generating data from simulation models is often costly and time consuming; thus, the number of input combinations in a study may be limiting even in simulation experiments. In this paper, we discuss the problem of designing computer simulation experiments that require generating data for just a fraction of the possible input combinations. We propose an approach that is based on subsampling the 1992 National Resources Inventory (NRI) points. We illustrate the procedure by assessing soil erosion in a situation where there are observed data (reported by the Natural Resources Conservation Service (NRCS)) for comparison. Estimates for soil erosion obtained using the procedure we propose are in good agreement with NRCS reported values
Climate and Soil Characteristics Determine Where No-Till Management Can Store Carbon in Soils and Mitigate Greenhouse Gas Emissions
Article number: 11665 (2019)201
Advancing national greenhouse gas inventories for agriculture in developing countries : improving activity data, emission factors and software technology
Peer reviewedPublisher PD
Climate and soil characteristics determine where no-till management can store carbon in soils and mitigate greenhouse gas emissions
Adoption of no-till management on croplands has become a controversial approach for storing carbon in soil due to conflicting findings. Yet, no-till is still promoted as a management practice to stabilize the global climate system from additional change due to anthropogenic greenhouse gas emissions, including the 4 per mille initiative promoted through the UN Framework Convention on Climate Change. We evaluated the body of literature surrounding this practice, and found that SOC storage can be higher under no-till management in some soil types and climatic conditions even with redistribution of SOC, and contribute to reducing net greenhouse gas emissions. However, uncertainties tend to be large, which may make this approach less attractive as a contributor to stabilize the climate system compared to other options. Consequently, no-till may be better viewed as a method for reducing soil erosion, adapting to climate change, and ensuring food security, while any increase in SOC storage is a co-benefit for society in terms of reducing greenhouse gas emissions.Facultad de Ciencias Agrarias y Forestale
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