Fooling Partial Dependence via Data Poisoning

Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances

Many parameters and positive-definiteness are two major obstacles in estimating and modelling a correlation matrix for longitudinal data. In addition, when longitudinal data is incomplete, incorrectly modelling the correlation matrix often results in bias in estimating mean regression parameters. In this paper, we introduce a flexible and parsimonious class of regression models for a covariance...

Deterministic Sparse Fourier Approximation via Fooling Arithmetic Progressions

A significant Fourier transform (SFT) algorithm, given a threshold τ and oracle access to a function f , outputs (the frequencies and approximate values of) all the τ -significant Fourier coefficients of f , i.e., the Fourier coefficients whose magnitude exceeds τ‖f‖2. In this paper we present the first deterministic SFT algorithm for functions f over ZN which is: (1) Local, i.e., its running t...

Conditional Dependence in Longitudinal Data Analysis

Mixed models are widely used to analyze longitudinal data. In their conventional formulation as linear mixed models (LMMs) and generalized LMMs (GLMMs), a commonly indispensable assumption in settings involving longitudinal non-Gaussian data is that the longitudinal observations from subjects are conditionally independent, given subject-specific random effects. Although conventional Gaussian...

Fooling-sets and rank

ABSTRACT. An n×n matrix M is called a fooling-set matrix of size n if its diagonal entries are nonzero and Mk,lMl,k = 0 for every k 6= l. Dietzfelbinger, Hromkovič, and Schnitger (1996) showed that n ≤ (rkM)2 , regardless of over which field the rank is computed, and asked whether the exponent on rkM can be improved. We settle this question. In characteristic zero, we construct an infinite fami...

Neoatherosclerosis: fooling mother nature.