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We prove that suitably generic pairs of linear equations on an even number of variables are uncommon. This verifies a conjecture of Kam\v{c}ev, Morrison and the second author. Moreover, we prove that any large system containing such a $(2\times k)$-system as a minimal subsystem is uncommon., Comment: 11 pages

A system of linear equations in $\mathbb{F}_p^n$ is \textit{Sidorenko} if any subset of $\mathbb{F}_p^n$ contains at least as many solutions to the system as a random set of the same density, asymptotically as $n\to \infty$. A system of linear equations is \textit{common} if any 2-colouring of $\mathbb{F}_p^n$ yields at least as many monochromatic solutions to the system of equations as a random 2-colouring, asymptotically as $n\to \infty$. Both classification problems remain wide open despite recent attention. We show that a certain generic family of systems of two linear equations is not Sidorenko. In fact, we show that systems in this family are not locally Sidorenko, and that systems in this family which do not contain additive tuples are not weakly locally Sidorenko. This endeavour answers a conjecture and question of Kam\v{c}ev--Liebenau--Morrison. Insofar as methods, we observe that the true complexity of a linear system is not maintained under Fourier inversion; our main novelty is the use of higher-order methods in the frequency space of systems which have complexity one. We also give a shorter proof of the recent result of Kam\v{c}ev--Liebenau--Morrison and independently Versteegen that any linear system containing a four term arithmetic progression is uncommon., Comment: 20 pages

We prove that any increasing sequence of real numbers with average gap $1$ and Poisson pair correlations has some gap that is at least $3/2+10^{-9}$. This improves upon a result of Aistleitner, Blomer, and Radziwill., Comment: 17 pages

A system of linear equations in $\mathbb{F}_p^n$ is \textit{common} if every two-colouring of $\mathbb{F}_p^n$ yields at least as many monochromatic solutions as a random two-colouring, asymptotically as $n \to \infty$. By analogy to the graph-theoretic setting, Alon has asked whether any (non-Sidorenko) system of linear equations can be made uncommon by adding sufficiently many free variables. Fox, Pham and Zhao answered this question in the affirmative among systems which consist of a single equation. We answer Alon's question in the negative. We also observe that the property of remaining common despite that addition of arbitrarily many free variables is closely related to a notion of commonness in which one replaces the arithmetic mean of the number of monochromatic solutions with the geometric mean, and furthermore resolve questions of Kam\v{c}ev--Liebenau--Morrison., Comment: 12 pages, 1 figure

Green and Tao's arithmetic regularity lemma and counting lemma together apply to systems of linear forms which satisfy a particular algebraic criterion known as the `flag condition'. We give an arithmetic regularity lemma and counting lemma which applies to all systems of linear forms., Comment: 47 pages

Gowers and Wolf have conjectured that, given a set of linear forms $\{\psi_i\}_{i=1}^t$ each mapping $\mathbb{Z}^D$ to $\mathbb{Z}$, if $s$ is an integer such that the functions $\psi_1^{s+1},\ldots, \psi_t^{s+1}$ are linearly independent, then averages of the form $\mathbb{E}_{\boldsymbol{x}} \prod_{i=1}^t f(\psi_i(\boldsymbol{x}))$ may be controlled by the Gowers $U^{s+1}$-norm of $f$. We prove (a stronger version of) this conjecture., Comment: 13 pages; minor changes based on referee comments; formatted for Discrete Analysis

We consider, over both the integers and finite fields, Szemer\'{e}di's theorem on $k$-term arithmetic progressions where the set $S$ of allowed common differences in those progressions is restricted and random. Fleshing out a line of enquiry suggested by Frantzikinakis et al, we show that over the integers, the conjectured threshold for $\mathbb{P}(d \in S)$ for Szemer\'{e}di's theorem to hold a.a.s follows from a conjecture about how so-called dual functions are approximated by nilsequences. We also show that the threshold over finite fields is different to this threshold over the integers., Comment: 14 pages, minor changes from previous version

Let $\mathcal{G}(n,r,s)$ denote a uniformly random $r$-regular $s$-uniform hypergraph on $n$ vertices, where $s$ is a fixed constant and $r=r(n)$ may grow with $n$. An $\ell$-overlapping Hamilton cycle is a Hamilton cycle in which successive edges overlap in precisely $\ell$ vertices, and 1-overlapping Hamilton cycles are called loose Hamilton cycles. When $r,s\geq 3$ are fixed integers, we establish a threshold result for the property of containing a loose Hamilton cycle. This partially verifies a conjecture of Dudek, Frieze, Rucinski and Sileikis (2015). In this setting, we also find the asymptotic distribution of the number of loose Hamilton cycles in $\mathcal{G}(n,r,s)$. Finally we prove that for $\ell = 2,\ldots, s-1$ and for $r$ growing moderately as $n\to\infty$, the probability that $\mathcal{G}(n,r,s)$ has a $\ell$-overlapping Hamilton cycle tends to zero., Comment: 66 pages, 8 figures. This version addresses referees' comments

To improve confounder adjustments, observational studies are often matched on potential confounders. While matched case-control studies are common and well covered in the literature, our focus here is on matched cohort studies, which are less common and sparsely discussed in the literature. Matched data also arise naturally in twin studies, as a cohort of exposure-discordant twins can be viewed as being matched on a large number of potential confounders. The analysis of twin studies will be given special attention. We give an overview of various analysis methods for matched cohort studies with binary exposures and binary outcomes. In particular, our aim is to answer the following questions: (1) What are the target parameters in the common analysis methods? (2) What are the underlying assumptions in these methods? (3) How do the methods compare in terms of statistical power?, Comment: Published in at http://dx.doi.org/10.1214/12-STS390 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Discriminant analysis is a multivariate method of analysis with two purposes: (1) to describe differences among groups; or (2) to classify participants into groups. Either linear or quadratic rules can be used in both descriptive discriminant analysis (DDA) and predictive discriminant analysis (PDA). In both DDA and PDA the researcher wants to use the rules that are the most accurate and the most replicable, but these two considerations often offer competing perspectives about which rule is optimal in a given analysis. The linear rule is used when group covariance matrices are pooled. The linear rule has greater external generalizability because fewer parameters are estimated. The quadratic rule is used when separate group covariance matrices are used. These covariance matrices do not need to be as homogenous. However, the quadratic rule's external generalizability is not as accurate. It is important to use the correct rule in both PDA and DDA. (Contains 10 references.) (Author/SLD)