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McElroy T.,Center for Statistical Research and Methodology
Journal of Forecasting

Although both direct multi-step-ahead forecasting and iterated one-step-ahead forecasting are two popular methods for predicting future values of a time series, it is not clear that the direct method is superior in practice, even though from a theoretical perspective it has lower mean squared error (MSE). A given model can be fitted according to either a multi-step or a one-step forecast error criterion, and we show here that discrepancies in performance between direct and iterative forecasting arise chiefly from the method of fitting, and is dictated by the nuances of the model's misspecification. We derive new formulas for quantifying iterative forecast MSE, and present a new approach for assessing asymptotic forecast MSE. Finally, the direct and iterative methods are compared on a retail series, which illustrates the strengths and weaknesses of each approach. Copyright © 2015 John Wiley & Sons, Ltd. Source

Klein M.D.,Center for Statistical Research and Methodology | Sinha B.K.,University of Maryland Baltimore County | Subramaniam R.P.,U.S. Environmental Protection Agency
Journal of Biopharmaceutical Statistics

Physiologically based pharmacokinetic (PBPK) modeling has reached considerable sophistication in its application to pharmacological and environmental health problems. Yet, mature methodologies for making statistical inferences have not been routinely incorporated in these applications except in a few data-rich cases. This paper demonstrates how improved statistical inference on estimated model parameters from both frequentist and Bayesian points of view can be routinely carried out. We work with a previously developed PBPK model for the formation and disposition of DNA-protein cross-links formed by inhaled formaldehyde in the nasal lining of rats and rhesus monkeys. We purposefully choose this model because it is based on sparse time-course data. Copyright © Taylor & Francis Group, LLC. Source

Janicki R.,Center for Statistical Research and Methodology | Malec D.,National Center for Health Statistics
Computational Statistics and Data Analysis

In many surveys, the goal is to estimate the proportion of the population with a certain characteristic of interest. This estimation problem is often complicated by survey nonresponse and the difficulty in modeling the nonresponse mechanism. In this paper, a new method is developed for analyzing categorical data with nonresponse when there is uncertainty about ignorability, which incorporates the idea that there are many a priori plausible ignorable and nonignorable nonresponse models. A class of saturated submodels of the full, nonidentifiable likelihood, containing models which have mixtures of ignorable and nonignorable components is considered, and Bayesian averaging is used to incorporate model uncertainty. This approach is then extended by using uniform priors on model components which do not fit into the partition structure. This method is illustrated using data from the 2000 Accuracy and Coverage Evaluation Survey. A simulation study is used to evaluate the performance of this method and to compare it to other popular nonignorable Bayesian models. The results of the simulation study show that the proposed method generates point estimates which can have reduced mean squared error, and credible intervals which are often, on average, narrower, and which contain the true value of the parameter more frequently, as compared to other nonignorable models, and hence provides a better method for quantifying the additional uncertainty due to the missing data. © 2012 Elsevier B.V. All rights reserved. Source

McElroy T.S.,Center for Statistical Research and Methodology | Holan S.H.,University of Missouri
Computational Statistics and Data Analysis

Gegenbauer processes allow for flexible and convenient modeling of time series data with multiple spectral peaks, where the qualitative description of these peaks is via the concept of cyclical long-range dependence. The Gegenbauer class is extensive, including ARFIMA, seasonal ARFIMA, and GARMA processes as special cases. Model estimation is challenging for Gegenbauer processes when multiple zeros and poles occur in the spectral density, because the autocovariance function is laborious to compute. The method of splitting-essentially computing autocovariances by convolving long memory and short memory dynamics-is only tractable when a single long memory pole exists. An additive decomposition of the spectrum into a sum of spectra is proposed, where each summand has a single singularity, so that a computationally efficient splitting method can be applied to each term and then aggregated. This approach differs from handling all the poles in the spectral density at once, via an analysis of truncation error. The proposed technique allows for fast estimation of time series with multiple long-range dependences, which is illustrated numerically and through several case-studies. Source

Depersio M.,Center for Disclosure Avoidance Research | Lemons M.,Center for Disclosure Avoidance Research | Ramanayake K.A.,Center for Disclosure Avoidance Research | Tsay J.,Center for Statistical Research and Methodology | Zayatz L.,Center for Disclosure Avoidance Research
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Data from the American Community Survey (ACS) are collected under the authority of Title 13 of the United States Code which guarantees the confidentiality of all survey respondents. To maintain this confidentiality while still being able to release usable data, the U.S. Census Bureau applies several disclosure avoidance methods. For the ACS, data swapping techniques are used to protect records deemed at risk. Households are identified as at risk if they are unique on attributes selected and predetermined by the confidentiality and survey management staff. After being uniquely identified (or flagged), data swapping is used to exchange the geographic information of a flagged household with another flagged household. This study compared the effectiveness of the pair-swapping method currently used with a proposed n-Cycle swapping method. Specifically, the goal was to maintain the same level of disclosure protection while outputting data with less perturbation. © 2012 Springer-Verlag Berlin Heidelberg. Source

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