BEGIN:VCALENDAR
X-WR-CALNAME:Webnotice (Statistics & Actuarial Science)
X-WR-CALDESC:Webnotice (Statistics & Actuarial Science) at University of Waterloo
X-PUBLISHED-TTL:PT60M
PRODID:-//UW-Webnotice/NONSGML 0.1//EN
VERSION:2.0
BEGIN:VEVENT
DTSTAMP:20211201T170000Z
UID:2021_70474bbad1357a7fc4d20ef01e8389e8.wnotice@math.uwaterloo.ca
DTSTART:20211201T170000Z
DTEND:20211201T180000Z
SUMMARY:Excursion sets and critical points of Gaussian random fields over high thresholds (Probabilty Seminar)
LOCATION:Virtually on Zoom
DESCRIPTION:Excursion sets and critical points of Gaussian random fields over high thresholds\nYi Shen, University of Waterloo\n\nLink to seminar: uwaterloo.zoom.us/j/95473768262?pwd=VDR3a1ZSbmdaRFAzRVc1TzNUTnRHdz09\n\nIn this talk we discuss the excursion sets and the location and type of the critical points of isotropic Gaussian random fields satisfying certain conditions over high thresholds. We show that for these Gaussian random fields, when the threshold tends to infinity and the searching area expands with a matching speed, both the location of the excursion sets and the location of the local maxima above the threshold converge weakly to a Poisson point process. We will further discuss the possibility to approximate these locations when the threshold is high but not extremely high, by studying the local behavior of the critical points above the threshold of the random field. This is a joint work with Paul Marriott and Weinan Qi.
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BEGIN:VEVENT
DTSTAMP:20211202T210000Z
UID:2021_38bf5fc9a965312b994ae87bba07f4e5.wnotice@math.uwaterloo.ca
DTSTART:20211202T210000Z
DTEND:20211202T220000Z
SUMMARY:Functional random effects modeling of complex brain data (Statistics and Biostatistics Seminar)
LOCATION:Virtually on Zoom
DESCRIPTION:Functional random effects modeling of complex brain data\nEardi Lila, University of Washington\n\nLink to seminar: https://uwaterloo.zoom.us/j/95958511224?pwd=ZjBxTWYwem9DRGtUbjdjem5sNUs3QT09 Passcode: 800775\n\nHuman brains differ in their structural and functional organization across individuals. There is a long history of trying to relate either structural or functional brain features to human aspects, such as behavioral and cognitive variables. However, more recently, increasing attention has been drawn to the problem of understanding how brain structure and function are related to each other. Motivated by such a problem, I will introduce a statistical framework that can jointly model structural and functional brain image data. We will moreover adopt a Riemannian modeling approach to account for the non-linear geometric constraints that naturally arise in this setting and embed a random-effects component in order to disentangle genetic sources of variability from those driven by unique environmental factors. I will then show the results obtained by applying the proposed model to the Human Connectome Project dataset to explore spontaneous co-variation between brain shape and connectivity in young healthy individuals.
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BEGIN:VEVENT
DTSTAMP:20211206T160000Z
UID:2021_b899bce365fbdb9392d5238c1a6695e0.wnotice@math.uwaterloo.ca
DTSTART:20211206T160000Z
DTEND:20211206T170000Z
SUMMARY:A Marked Spatial Point Process for Insurance Claims Management (Seminar)
LOCATION:Virtually on Zoom
DESCRIPTION:A Marked Spatial Point Process for Insurance Claims Management\nLisa Gao, University of Wisconsin-Madison\n\nLink to join seminar: https://uwaterloo.zoom.us/j/98549900832?pwd=Q2RZWFVJT08rWVZuM3V1L01FTGR2Zz09\n\nTechnological advances in data collection indicate growing potential for analytics to support efficient claims management. We demonstrate how insurers can incorporate high-resolution weather data to assess hail property damage immediately following a hailstorm. In particular, we propose a marked spatial point process for replicated point patterns to model the frequency and severity of hail damage insurance claims. The point process focuses on the geographical distribution of claims and allows insurers to simultaneously incorporate densely collected weather features and traditional policyholder-level rating characteristics, despite being observed from different locations. The marks concern the financial impact of a hailstorm, particularly the effects of dependence among claims. We employ a spatial factor copula to capture spatial dependence, allowing insurers to decompose sources of dependence when jointly characterizing claim severity. Using hail damage insurance claims data from a U.S. insurer, supplemented with hail radar maps and other spatially varying weather features, we show that incorporating granular data to model the development of claim reporting patterns helps insurers anticipate and manage claims more efficiently.
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BEGIN:VEVENT
DTSTAMP:20211208T170000Z
UID:2021_fc0b321f46f8d8e6a4ee8480690050c0.wnotice@math.uwaterloo.ca
DTSTART:20211208T170000Z
DTEND:20211208T180000Z
SUMMARY:The multispecies totally asymmetric zero range process and Macdonald polynomials (Probabilty Seminar)
LOCATION:Virtually on Zoom
DESCRIPTION:The multispecies totally asymmetric zero range process and Macdonald polynomials\nOlya Mandelshtam, University of Waterloo\n\nLink to join: https://uwaterloo.zoom.us/j/98397743283?pwd=NmlzQ24wSnJybVBtZm5CNGt4QUxMQT09 Passcode: 596142\n\nOver the last couple of decades, the theory of interacting particle systems has found some unexpected connections to orthogonal polynomials, symmetric functions, and various combinatorial structures. The asymmetric simple exclusion process (ASEP) has played a central role in this connection. \n\nRecently, Cantini, de Gier, and Wheeler found that the partition function of the multispecies ASEP on a circle is a specialization of a Macdonald polynomial $P_{\lambda}(X;q,t)$. Macdonald polynomials are a family of symmetric functions that are ubiquitous in algebraic combinatorics and specialize to or generalize many other important special functions. Around the same time, Martin gave a recursive formulation expressing the stationary probabilities of the ASEP on a circle as sums over combinatorial objects known as multiline queues, which are a type of queueing system. Shortly after, with Corteel and Williams we generalized Martin's result to give a new formula for $P_{\lambda}$ via multiline queues. \n\nThe modified Macdonald polynomials $\widetilde{H}_{\lambda}(X;q,t)$ are a version of $P_{\lambda}$ with positive integer coefficients. A natural question was whether there exists a related statistical mechanics model for which some specialization of $\widetilde{H}_{\lambda}$ is equal to its partition function. With Ayyer and Martin, we answer this question in the affirmative with the multispecies totally asymmetric zero-range process (TAZRP), which is a specialization of a more general class of zero range particle processes. We introduce a new combinatorial object in the flavor of the multiline queues, which on one hand, expresses stationary probabilities of the mTAZRP, and on the other hand, gives a new formula for $\widetilde{H}_{\lambda}$. We define an enhanced Markov chain on these objects that lumps to the multispecies TAZRP, and then use this to prove several results about particle densities and correlations in the TAZRP.
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20211208T210000Z
UID:2021_996fff5ad24400c2ab5b7a74a849d6fc.wnotice@math.uwaterloo.ca
DTSTART:20211208T210000Z
DTEND:20211208T220000Z
SUMMARY:Estimating Time-varying Brain Connectivity (Seminar)
LOCATION:Virtually on Teams
DESCRIPTION:Estimating Time-varying Brain Connectivity\nJie Jian, PhD Student in Statistics, University of Waterloo\n\nLink to join: https://teams.microsoft.com/l/message/19:e20d28d0b2504114ab3f27015640e232@thread.tacv2/1638383691478?tenantId=723a5a87-f39a-4a22-9247-3fc240c01396&groupId=112a7f2a-e302-43f3-b571-01b44b66936b&parentMessageId=1638383691478&teamName=SAS%20Student%20Seminars&channelName=General&createdTime=1638383691478\n\nThe human brain is a complex dynamical system composed of many interacting regions. Knowledge of these interactions is the cornerstone for understanding the brain functional architecture and the relationship between neural dynamics. Brain connectivity can be represented as a network composed of a set of random variables (nodes) interconnected by a set of interactions (edges). As the brain is actively yielding thoughts and ideas, along with changes in arousal, awareness, and vigilance, modelling brain connectivity as a static network where a single snapshot of the network is observed can be misleading. We study time-varying networks and model the brain data as realizations from multivariate Gaussian distributions with precision matrices that change over time. To facilitate parameter estimation, we require not only that each precision matrix at any given time point be sparse, but also that precision matrices at neighboring time points be similar. We accomplish this by generalizing the Elastic Net of Zou and Hastie (2005) and the Fused LASSO of Tibshirani et al. (2005), and solving the resulting optimization problems with an efficient ADMM-based algorithm that utilizes blockwise fast computation. This is joint work with Dr. Peijun Sang and Dr. Mu Zhu.
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20211210T170000Z
UID:2021_53a643eac68112e72339ba4dbf4e3b84.wnotice@math.uwaterloo.ca
DTSTART:20211210T170000Z
DTEND:20211210T180000Z
SUMMARY:Statistical Learning and Matching Markets (Seminar)
LOCATION:Virtually on Zoom
DESCRIPTION:Statistical Learning and Matching Markets\nXiaowu Dai, University of California, Berkeley\n\nLink to join: https://uwaterloo.zoom.us/j/92939135090?pwd=YXExTXJEaytjWnNZNjFMU09nWUoxQT09 Passcode: 151909\n\nWe study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori and must be learned from data. Taking the two-sided matching market as a running example, we focus on the decentralized setting, where agents do not share their learned preferences with a central authority. Our approach is based on the representation of preferences in a reproducing kernel Hilbert space, and a learning algorithm for preferences that accounts for uncertainty due to the competition among the agents in the market. Under regularity conditions, we show that our estimator of preferences converges at a minimax optimal rate. Given this result, we derive optimal strategies that maximize agents' expected payoffs and we calibrate the uncertain state by taking opportunity costs into account. We also derive an incentive-compatibility property and show that the outcome from the learned strategies has a stability property. Finally, we prove a fairness property that asserts that there exists no justified envy according to the learned strategies. \n\nThis is a joint work with Michael I. Jordan.
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BEGIN:VEVENT
DTSTAMP:20211215T170000Z
UID:2021_9402defe327d527e026432628916a42d.wnotice@math.uwaterloo.ca
DTSTART:20211215T170000Z
DTEND:20211215T180000Z
SUMMARY:Understanding Statistical-vs-Computational Tradeoffs via Low-Degree Polynomials (Seminar)
LOCATION:Virtually on Zoom
DESCRIPTION:Understanding Statistical-vs-Computational Tradeoffs via Low-Degree Polynomials\nAlex Wein, University of California, Berkeley\n\nLink to join: https://uwaterloo.zoom.us/j/93047170869?pwd=Y2Z5citRZC9KV21QekJ0ajhRMEQ1QT09 Passcode: 741683\n\nMany high-dimensional statistical tasks---including sparse PCA (principal component analysis), community detection, gaussian clustering, tensor PCA, and more---exhibit a striking gap between what can be achieved statistically (by a brute force algorithm) and what can be achieved with the best known polynomial-time algorithms. An emerging framework to understand these gaps is based on analyzing the class of "low-degree polynomial algorithms". This is a powerful class of algorithms (which includes, for instance, spectral methods and approximate message passing), and so provable failure of this class of algorithms is a form of concrete evidence for inherent computational hardness of statistical problems. \n\nWhile low-degree algorithms were initially studied in the context of hypothesis testing problems, I will present some new general-purpose techniques for determining the precise limitations of low-degree algorithms for other types of statistical tasks, including estimation and optimization. I will focus primarily on two problems: recovering a planted submatrix of elevated mean in a gaussian matrix, and finding a large independent set in a sparse random graph. This line of work illustrates that low-degree polynomials provide a unifying framework for studying the computational complexity of a wide variety of statistical tasks. \n\nBased primarily on joint works with David Gamarnik, Aukosh Jagannath, and Tselil Schramm.
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