593,598 research outputs found

    Bounds on Quantiles in the Presence of Full and Partial Item Nonresponse

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    Microeconomic surveys are usually subject to the problem of item nonresponse, typically associated with variables like income and wealth, where confidentiality and/or lack of accurate information can affect the response behavior of the individual. Follow up categorical questions can reduce item nonresponse and provide additional partial information on the missing value, hence improving the quality of the data. In this paper we allow item nonresponse to be non-random and extend Manski’s approach of estimating bounds to identify an upper and lower limit for the parameter of interest (the distribution function or its quantiles). Our extension consists of deriving bounding intervals taking into account all three types of response behavior: full response, partial (categorical) response and full nonresponse. We illustrate the theory by estimating bounds for the quantiles of the distribution of amounts held in savings accounts. We consider worst case bounds which cannot be improved upon without additional assumptions, as well as bounds that follow from different assumptions of monotonicity.item nonresponse;bracket response;bounds and identification

    Generation interval contraction and epidemic data analysis

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    The generation interval is the time between the infection time of an infected person and the infection time of his or her infector. Probability density functions for generation intervals have been an important input for epidemic models and epidemic data analysis. In this paper, we specify a general stochastic SIR epidemic model and prove that the mean generation interval decreases when susceptible persons are at risk of infectious contact from multiple sources. The intuition behind this is that when a susceptible person has multiple potential infectors, there is a ``race'' to infect him or her in which only the first infectious contact leads to infection. In an epidemic, the mean generation interval contracts as the prevalence of infection increases. We call this global competition among potential infectors. When there is rapid transmission within clusters of contacts, generation interval contraction can be caused by a high local prevalence of infection even when the global prevalence is low. We call this local competition among potential infectors. Using simulations, we illustrate both types of competition. Finally, we show that hazards of infectious contact can be used instead of generation intervals to estimate the time course of the effective reproductive number in an epidemic. This approach leads naturally to partial likelihoods for epidemic data that are very similar to those that arise in survival analysis, opening a promising avenue of methodological research in infectious disease epidemiology.Comment: 20 pages, 5 figures; to appear in Mathematical Bioscience

    Bounds on Quantiles in the Presence of Full and Partial Item Nonresponse

    Get PDF
    Microeconomic surveys are usually subject to the problem of item nonresponse, typically associated with variables like income and wealth, where confidentiality and/or lack of accurate information can affect the response behavior of the individual. Follow up categorical questions can reduce item nonresponse and provide additional partial information on the missing value, hence improving the quality of the data. In this paper we allow item nonresponse to be non-random and extend Manski’s approach of estimating bounds to identify an upper and lower limit for the parameter of interest (the distribution function or its quantiles). Our extension consists of deriving bounding intervals taking into account all three types of response behavior: full response, partial (categorical) response and full nonresponse. We illustrate the theory by estimating bounds for the quantiles of the distribution of amounts held in savings accounts. We consider worst case bounds which cannot be improved upon without additional assumptions, as well as bounds that follow from different assumptions of monotonicity

    Countable locally 2-arc-transitive bipartite graphs

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    We present an order-theoretic approach to the study of countably infinite locally 2-arc-transitive bipartite graphs. Our approach is motivated by techniques developed by Warren and others during the study of cycle-free partial orders. We give several new families of previously unknown countably infinite locally-2-arc-transitive graphs, each family containing continuum many members. These examples are obtained by gluing together copies of incidence graphs of semilinear spaces, satisfying a certain symmetry property, in a tree-like way. In one case we show how the classification problem for that family relates to the problem of determining a certain family of highly arc-transitive digraphs. Numerous illustrative examples are given.Comment: 29 page

    Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach

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    The focus of modern biomedical studies has gradually shifted to explanation and estimation of joint effects of high dimensional predictors on disease risks. Quantifying uncertainty in these estimates may provide valuable insight into prevention strategies or treatment decisions for both patients and physicians. High dimensional inference, including confidence intervals and hypothesis testing, has sparked much interest. While much work has been done in the linear regression setting, there is lack of literature on inference for high dimensional generalized linear models. We propose a novel and computationally feasible method, which accommodates a variety of outcome types, including normal, binomial, and Poisson data. We use a "splitting and smoothing" approach, which splits samples into two parts, performs variable selection using one part and conducts partial regression with the other part. Averaging the estimates over multiple random splits, we obtain the smoothed estimates, which are numerically stable. We show that the estimates are consistent, asymptotically normal, and construct confidence intervals with proper coverage probabilities for all predictors. We examine the finite sample performance of our method by comparing it with the existing methods and applying it to analyze a lung cancer cohort study
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