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Running off the £2 trillion of UK corporate sector defined benefit liabilities in an efficient and effective fashion is the biggest challenge facing the UK pensions industry. As more and more defined benefit pension schemes start maturing, the trustees running those schemes need to consider what their target end-state will be and the associated journey plan. However, too few trustee boards have well-articulated and robust plans. Determining the target end-state requires a grasp of various disciplines and an ability to work collaboratively with different professional advisers. This paper sets out issues trustees, employers and their advisers can consider when addressing whether their target end state should be low- dependency, buyout or transfer to a superfund. Member outcomes analysis is introduced as a central tool through which to differentiate alternative target end-states. A five-step methodology is set out for deriving an optimal target end-state for a scheme. Also considered are the specific factors impacting stressed schemes, which highlights the importance to trustee boards when considering their Plan B should their employer or scheme ever become stressed. The paper ends with specific recommendations for the actuarial profession and The Pensions Regulator to take forward.
This chapter explains weighting in a manner that allows us to appreciate both the power and vulnerability of the technique and, by extension, other techniques that rely on similar assumptions. Once we understand how weighting works, we will better understand when it works. This chapter opens by discussing weighting in general terms. The subsequent sections get more granular. Sections 3.2 and 3.3 cover widely used weighting techniques: cell-weighting and raking. Section 3.4 covers variable selection, a topic that may well be more important than weighting technique. Section 3.5 covers the effect of weighting on precision, a topic that frequently gets lost in polling reporting. This chapter mixes intuitive and somewhat technical descriptions of weighting. The technical details in Sections 3.2 and3.3 can be skimmed by readers focused on the big picture how weighting works.
This chapter introduces selection models in a way that highlights important intuition about how they work. Section 8.1 formalizes the model we’ve been working with already. Section 8.2 uses the model to highlight a bad news, good news story. The bad news is that statistical estimation of a two-equation model like this will be challenging. The good news is that the model helps us recognize the traces nonignorable nonresponse leaves in observable data. Section 8.3 introduces the Heckman selection model. Section 8.4 uses the Heckman model to highlight the starkly different way that selection and weighting approaches use information. The Heckman model is far from perfect, however, as Section 8.5 explains.
This chapter explores the challenges of polling in light caused by nonignorable nonresponse. Nonprobability polling approach comes off poorly for reasons that harken back to the Literary Digest fiasco. The random sampling is far from perfect, but here we rename it the random contact approach – because what is random is who they contact, not who responds once contacted – and show that using random contact shifts error from being proportional to the population size – which can be catastrophic – to being proportional to response rates – which is not great, but much better. Section 6.1 assesses the big data approach by introducing the idea of effective sample size, a concept that allows us to compare potentially large nonrandom samples to their random sampling equivalents. Section 6.2 assesses the random contact approach that has become the last refuge of those clinging to the random sampling paradigm. Section 6.3 decomposes sampling error into elements associated with the choosing whom to contact and elements associated with individual choices given that they are contacted. This section helps clarify where the biggest threats are throughout the survey process.
Polling has become very difficult. People do not respond, and pollsters use methods that are far removed from the random sampling tools that built the field. This chapter introduces the book by outlining the main challenges facing polling today, how conventional tools fail to fully meet these challenges and how a new paradigm and new methods can more directly take on the full spectrum of nonresponse bias given contemporary polling practices.
This chapter highlights the critical importance of having the right kind of data for selection models that address nonignorable nonresponse. In general, we need a variable that is included in our response model and excluded from our outcome model. The best approach is creating a randomized response instrument that affects whether someone responds, but does not affect the content of their response. In many polling contexts, it is easy to create randomized response instruments. The pollster simply needs to figure out some protocol that affects response rate and then randomize it. Section 10.1 makes it clear that knowing the correct functional form is not enough to save a selection model. Section 10.2 highlights the difficulty of using observational response instruments. Section 10.3 discusses how and why to create randomized response instruments. Section 10.4 shows how to use randomized response instruments in a simple test for diagnosing nonignorable nonresponse. Section 10.5 shows how randomized response instruments enable us to use the full suite of selection models even when we do not observe data for nonrespondents.
This paper proposes a nonparametric test to assess whether there exist heterogeneous quantile treatment effects (QTEs) of an intervention on the outcome of interest across different sub-populations defined by covariates of interest. Specifically, a consistent test statistic based on the Cramér–von Mises type criterion is developed to test if the treatment has a constant quantile effect for all sub-populations defined by covariates of interest. Under some regularity conditions, the asymptotic behaviors of the proposed test statistic are investigated under both the null and alternative hypotheses. Furthermore, a nonparametric Bootstrap procedure is suggested to approximate the finite-sample null distribution of the proposed test; then, the asymptotic validity of the proposed Bootstrap test is theoretically justified. Through Monte Carlo simulations, we demonstrate the power properties of the test in finite samples. Finally, the proposed testing approach is applied to investigate whether there exists heterogeneity for the QTE of maternal smoking during pregnancy on infant birth weight across different age groups of mothers.
We study a skew Ornstein–Uhlenbeck process with zero being a sticky reflecting boundary, which is defined as the weak solution to a stochastic differential equation (SDE) system involving local time. The main results obtained include: (i) the existence and uniqueness of solutions to the SDE system, (ii) the scale function and speed measure, and (iii) the distributional properties regarding the transition density and the first hitting times. On the application side, we apply the process to interest rate modeling and obtain the explicit pricing formula for zero-coupon bonds. Numerical examples illustrate the impacts on bond yields of skewness and stickiness parameters.
We show that for every $\eta \gt 0$ every sufficiently large $n$-vertex oriented graph $D$ of minimum semidegree exceeding $(1+\eta )\frac k2$ contains every balanced antidirected tree with $k$ edges and bounded maximum degree, if $k\ge \eta n$. In particular, this asymptotically confirms a conjecture of the first author for long antidirected paths and dense digraphs.
Further, we show that in the same setting, $D$ contains every $k$-edge antidirected subdivision of a sufficiently small complete graph, if the paths of the subdivision that have length $1$ or $2$ span a forest. As a special case, we can find all antidirected cycles of length at most $k$.
Finally, we address a conjecture of Addario-Berry, Havet, Linhares Sales, Reed, and Thomassé for antidirected trees in digraphs. We show that this conjecture is asymptotically true in $n$-vertex oriented graphs for all balanced antidirected trees of bounded maximum degree and of size linear in $n$.
A result of Gyárfás [12] exactly determines the size of a largest monochromatic component in an arbitrary $r$-colouring of the complete $k$-uniform hypergraph $K_n^k$ when $k\geq 2$ and $k\in \{r-1,r\}$. We prove a result which says that if one replaces $K_n^k$ in Gyárfás’ theorem by any ‘expansive’ $k$-uniform hypergraph on $n$ vertices (that is, a $k$-uniform hypergraph $G$ on $n$ vertices in which $e(V_1, \ldots, V_k)\gt 0$ for all disjoint sets $V_1, \ldots, V_k\subseteq V(G)$ with $|V_i|\gt \alpha$ for all $i\in [k]$), then one gets a largest monochromatic component of essentially the same size (within a small error term depending on $r$ and $\alpha$). As corollaries we recover a number of known results about large monochromatic components in random hypergraphs and random Steiner triple systems, often with drastically improved bounds on the error terms.
Gyárfás’ result is equivalent to the dual problem of determining the smallest possible maximum degree of an arbitrary $r$-partite $r$-uniform hypergraph $H$ with $n$ edges in which every set of $k$ edges has a common intersection. In this language, our result says that if one replaces the condition that every set of $k$ edges has a common intersection with the condition that for every collection of $k$ disjoint sets $E_1, \ldots, E_k\subseteq E(H)$ with $|E_i|\gt \alpha$, there exists $(e_1, \ldots, e_k)\in E_1\times \cdots \times E_k$ such that $e_1\cap \cdots \cap e_k\neq \emptyset$, then the smallest possible maximum degree of $H$ is essentially the same (within a small error term depending on $r$ and $\alpha$). We prove our results in this dual setting.
For a graph $H$ and a hypercube $Q_n$, $\textrm{ex}(Q_n, H)$ is the largest number of edges in an $H$-free subgraph of $Q_n$. If $\lim _{n \rightarrow \infty } \textrm{ex}(Q_n, H)/|E(Q_n)| \gt 0$, $H$ is said to have a positive Turán density in a hypercube or simply a positive Turán density; otherwise, it has zero Turán density. Determining $\textrm{ex}(Q_n, H)$ and even identifying whether $H$ has a positive or zero Turán density remains a widely open question for general $H$. By relating extremal numbers in a hypercube and certain corresponding hypergraphs, Conlon found a large class of graphs, ones having so-called partite representation, that have zero Turán density. He asked whether this gives a characterisation, that is, whether a graph has zero Turán density if and only if it has partite representation. Here, we show that, as suspected by Conlon, this is not the case. We give an example of a class of graphs which have no partite representation, but on the other hand, have zero Turán density. In addition, we show that any graph whose every block has partite representation has zero Turán density in a hypercube.
In this paper, the ordering properties of convex and increasing convex orders of the dependent random variables are studied. Some closure properties of the convex and increasing convex orders under independent random variables are extended to the dependent random variables under the Archimedean copula. Two applications are provided to illustrate our results.
Tao and Vu showed that every centrally symmetric convex progression $C\subset \mathbb{Z}^d$ is contained in a generalized arithmetic progression of size $d^{O(d^2)} \# C$. Berg and Henk improved the size bound to $d^{O(d\log d)} \# C$. We obtain the bound $d^{O(d)} \# C$, which is sharp up to the implied constant and is of the same form as the bound in the continuous setting given by John’s theorem.
In February 2021, a cluster of Beta variant (B.1.351) coronavirus disease 2019 (COVID-19) cases were identified in an apartment building located in Northern Ontario, Canada. Most cases had no known contact with each other. Objectives of this multi-component outbreak investigation were to better understand the social and environmental factors that facilitated the transmission of COVID-19 through this multi-unit residential building (MURB). A case–control study examined building-specific exposures and resident behaviours that may have increased the odds of being a case. A professional engineer assessed the building’s heating, ventilation, and air-conditioning (HVAC) systems. Whole-genome sequencing and an in-depth genomic analysis were performed. Forty-five outbreak-confirmed cases were identified. From the case–control study, being on the upper floors (OR: 10.4; 95% CI: 1.63–66.9) and within three adjacent vertical lines (OR: 28.3; 3.57–225) were both significantly associated with being a case of COVID-19, after adjusting for age. There were no significant differences in reported behaviours, use of shared spaces, or precautions taken between cases and controls. An assessment of the building’s ventilation found uncontrolled air leakage between apartment units. A single genomic cluster was identified, where most sequences were identical to one another. Findings from the multiple components of this investigation are suggestive of aerosol transmission between units.