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Differentially Private Inference for Binomial Data
In: Journal of privacy and confidentiality, Band 10, Heft 1
ISSN: 2575-8527
We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite sample performance. We show that in general, DP hypothesis tests can be written in terms of linear constraints, and for exchangeable data can always be expressed as a function of the empirical distribution. Using this structure, we prove a `Neyman-Pearson lemma' for binomial data under DP, where the DP-UMP only depends on the sample sum. Our tests can also be stated as a post-processing of a random variable, whose distribution we coin ``Truncated-Uniform-Laplace'' (Tulap), a generalization of the Staircase and discrete Laplace distributions. Furthermore, we obtain exact p-values, which are easily computed in terms of the Tulap random variable.
Using the above techniques, we show that our tests can be applied to give uniformly most accurate one-sided confidence intervals and optimal confidence distributions. We also derive uniformly most powerful unbiased (UMPU) two-sided tests, which lead to uniformly most accurate unbiased (UMAU) two-sided confidence intervals. We show that our results can be applied to distribution-free hypothesis tests for continuous data. Our simulation results demonstrate that all our tests have exact type I error, and are more powerful than current techniques.
Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes
In: IZA Discussion Paper No. 15236
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On Binscatter
In: FRB of New York Staff Report No. 881, Rev. November 2023
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Working paper
Uniform and Lp Convergences for Nonparametric Continuous Time Regressions With Semiparametric Applications
In: Journal of Econometrics, Band 235, Heft 2
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Efficient random variable generation: ratio of uniforms and polar rejection sampling
Monte Carlo techniques, which require the generation of samples from some target density, are often the only alternative for performing Bayesian inference. Two classic sampling techniques to draw independent samples are the ratio of uniforms (RoU) and rejection sampling (RS). An efficient sampling algorithm is proposed combining the RoU and polar RS (i.e. RS inside a sector of a circle using polar coordinates). Its efficiency is shown in drawing samples from truncated Cauchy and Gaussian random variables, which have many important applications in signal processing and communications. ; This work has been partly financed by the Spanish government, through the DEIPRO project (TEC2009-14504-C02-01) and the CONSOLIDER-INGENIO 2010 program (CSD2008-00010). ; Publicado
BASE
Bayesian Meta-Analysis of Social Network Data via Conditional Uniform Graph Quantiles
In: Sociological methodology, Band 41, Heft 1, S. 257-298
ISSN: 1467-9531
Many basic questions in the social network literature center on the distribution of aggregate structural properties within and across populations of networks. Such questions are of increasing relevance given the growing availability of network data suitable for meta-analytic studies, as well as the rise of study designs that involve the collection of data on multiple networks drawn from a larger population. Despite this, little work has been done on model-based inference for the properties of graph populations, or on methods for comparing such populations. Here, we attempt to rectify this gap by introducing a family of techniques that combines an existing approach to the identification of structural biases in network data (the use of conditional uniform graph quantiles) with strategies drawn from nonparametric Bayesian analysis. Conditional uniform graph quantiles are the quantiles of an observed structural property in the reference distribution produced by evaluating that property over all graphs with certain fixed characteristics (e.g., size or density). These quantiles have long been used to measure the extent to which a property of interest on a single network deviates from what would be expected given that network's other characteristics. The methods introduced here employ such quantile information to allow for principled inference regarding the distribution of structural biases within (and comparison across) populations of networks, given data sampled at the network level. The data requirements of these methods are minimal, thus making them well-suited to meta-analytic applications for which complete network data (as opposed to summary statistics) are often unavailable. The structural biases inferred using these methods can be expressed in terms of posterior predictives for familiar and easily communicated quantities, such as p-values. In addition to the methods themselves, we present algorithms for posterior simulation from this model class, illustrating their use with applications to the analysis of social structure within urban communes and radio communications among emergency personnel. We also discuss how this approach may applied to quantiles arising from other reference distributions, such as those obtained using general exponential-family random graph models.
Inference on the maximal rank of time-varying covariance matrices using high-frequency data
We study the rank of the instantaneous or spot covariance matrix ΣX(t) of a multidimensional continuous semi-martingale X(t). Given highfrequency observations X(i/n), i = 0,.,n, we test the null hypothesis rank (ΣX(t)) <= r for all t against local alternatives where the average (r + 1)st eigenvalue is larger than some signal detection rate vn. A major problem is that the inherent averaging in local covariance statistics produces a bias that distorts the rank statistics. We show that the bias depends on the regularity and a spectral gap of ΣX(t).We establish explicit matrix perturbation and concentration results that provide non-asymptotic uniform critical values and optimal signal detection rates vn. This leads to a rank estimation method via sequential testing. For a class of stochastic volatility models, we determine data-driven critical values via normed p-variations of estimated local covariance matrices. The methods are illustrated by simulations and an application to high-frequency data of U.S. government bonds.
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Simultaneous Inference for the Partially Linear Model with A Multivariate Unknown Function When the Covariates are Measured with Errors
In: SFB 649 Discussion Paper 2016-024
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On a Transform for Modeling Skewness
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A Simple and Robust Estimator for Linear Regression Models with Strictly Exogenous Instruments
In: CAEPR Working Paper #2017-001
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Tendency Evidence in Hughes v The Queen: Similarity, Probative Value and Admissibility
In: Sydney Law Review, Band 38, Heft 4, S. 491-503
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Honest Confidence Sets in Nonparametric IV Regression and Other Ill-Posed Models
In: Econometric Theory (2020), 36(4)
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Working paper