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

Escherichia coli O157:H7 bacteriophage Φ241 isolated from an industrial cucumber fermentation at high acidity and salinity

A novel phage, Φ241, specific for Escherichia coli O157:H7 was isolated from an industrial cucumber fermentation where both acidity (pH ≤ 3.7) and salinity (≥5% NaCl) were high. The phage belongs to the Myoviridae family. Its latent period was 15 min and average burst size was 53 phage particles per infected cell. The phage was able to lyse 48 E. coli O157:H7 strains, but none of the 18 non-O157 strains (including E. coli O104:H7) or the 2 O antigen-negative mutants of O157:H7 strain, 43895Δper (also lacking H7 antigen) and F12 (still expressing H7 antigen). However, the phage was able to lyse a per-complemented strain (43895ΔperComp) which expresses O157 antigen. These results indicated that phage Φ241 is specific for O157 antigen, and E. coli strains lacking O157 antigen were resistant to the phage infection, regardless of the presence or absence of H7 antigen. SDS-PAGE profile revealed at least 13 structural proteins of the phage. The phage DNA was resistant to many commonly used restriction endonucleases, suggesting the presence of modified nucleotides in the phage genome. At the multiplicity of infection of 10, 3, or 0.3, the phage caused a rapid cell lysis within 1 or 2 h, resulting in 3.5- or 4.5-log-unit reduction in cell concentration. The high lytic activity, specificity and tolerance to low pH and high salinity make phage Φ241 a potentially ideal biocontrol agent of E. coli O157:H7 in various foods. To our knowledge, this is the first report on E. coli O157:H7 phage isolated from high acidity and salinity environment

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

The fractional volatility model: An agent-based interpretation

Based on criteria of mathematical simplicity and consistency with empirical market data, a model with volatility driven by fractional noise has been constructed which provides a fairly accurate mathematical parametrization of the data. Here, some features of the model are discussed and, using agent-based models, one tries to find which agent strategies and (or) properties of the financial institutions might be responsible for the features of the fractional volatility model.Comment: 23 pages, 11 figure

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