Archives: 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019

• Inference on weak signals in presence of an additive noise

  Speaker: Abram Kagan (Dept. of Math. (Statistics program)) - http://math.umd.edu/~amk
  

  When: Thu, September 6, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  
• Semiparametric Transformation Probit Models with Current-Status Data

  Speaker: Dr. Jing Qin (National Institutes of Health) -
  

  When: Thu, September 13, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  
• Generalized Group Testing: Some Results and Open Problems

  Speaker: Yaakov Malinovsky (Dept. of Math. and Stat., UMBC) -
  

  When: Thu, September 20, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  
• Biostatistical Methods for Wearable and Implantable Technology (WIT)

  Speaker: Prof. Ciprian Crainiceanu (Dept. of Biostatistics, Johns Hopkins University) -
  

  When: Thu, September 27, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  

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  Abstract: Wearable and Implantable Technology (WIT) is rapidly changing the Biostatistics data analytic landscape due to their reduced bias and measurement error as well as to the sheer size and complexity of the signals. In this talk I will review some of the most used and useful sensors in Health Sciences and the ever-expanding WIT analytic environment. I will describe the use of WIT sensors including accelerometers, heart monitors, glucose monitors and their combination with ecological momentary assessment (EMA). This rapidly expanding data eco-system is characterized by multivariate densely sampled time series with complex and highly non-stationary structures. I will introduce an array of scientific problems that can be answered using WIT and I will describe methodsdesigned to analyze the WIT data from the micro- (sub-second-level) to the macro-scale (minute-, hour- or day-level) data.
  
• An efficient procedure to combine biomarkers with limits of detection for risk prediction

  Speaker: Dr. Ruth Pfeiffer (National Cancer Institute, NIH) -
  

  When: Thu, October 11, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  

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  Abstract: Much research seeks biomarkers for diagnosing disease and understanding disease etiology.
  As high-throughput technologies allow measuring multiple markers simultaneously, strategies for combining markers are needed, particularly if no single marker is highly discriminating. Statistical procedures to combine information from multiple markers need to account for correlations and for left and/or right censoring of the markers due to lower or upper limits of detection of the laboratory assays. We thus extend dimension reduction approaches, specifically likelihood-based sufficient dimension reduction, to regression or classification with censored predictors. Using an expectation maximization (EM) algorithm, we find linear combinations that contain all or most of the information contained in correlated markers for modeling and prediction of an outcome variable, while accounting for left and right censoring due to detection limits. We also allow for selection of important variables through penalization. We assess the performance of our methods extensively in simulations and apply them to data from a study conducted to assess associations of 47 inflammatory markers and lung cancer risk and build prediction models.
  
  This is joint work with Diego Tomassi, Liliana Forzani and Efstathia Bura
  
  
• Accuracy of High-Dimensional Deep Learning Networks

  Speaker: Jason Klusowski (Dept. of Statistics, Rutgers University) -
  

  When: Tue, October 16, 2018 - 3:30pm
  Where: Kirwan Hall 1313 (notice change of time)
  

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  Abstract: `It has been experimentally observed in recent years that
  multi-layer artificial neural networks have a surprising ability to
  generalize, even when trained with far more parameters than
  observations. Is there a theoretical basis for this? The best available
  bounds on their metric entropy and associated complexity measures are
  essentially linear in the number of parameters, which is inadequate to
  explain this phenomenon. Here we examine the statistical risk (mean
  squared predictive error) of multi-layer networks with $\ell^1$-type
  controls on their parameters and with ramp activation functions (also
  called lower-rectified linear units). In this setting, the risk is shown
  to be upper bounded by $[(L^3 \log d)/n]^{1/2}$, where $d$ is the input
  dimension to each layer, $L$ is the number of layers, and $n$ is the
  sample size. In this way, the input dimension can be much larger than
  the sample size and the estimator can still be accurate, provided the
  target function has such $\ell^1$ controls and that the sample size is
  at least moderately large compared to $L^3\log d$. The heart of the
  analysis is the development of a sampling strategy that demonstrates the
  accuracy of a sparse covering of deep ramp networks. Lower bounds show
  that the identified risk is close to being optimal. This is joint work
  with Andrew R. Barron.''
  
• On the construction of unbiased estimators for the group testing problem

  Speaker: Dr. Gregory Hader (National Cancer Institute, NIH) -
  

  When: Thu, October 25, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  

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  Abstract:
  
  Abstract
  
  While the use of group testing as a tool for estimation has been on the rise in recent decades, classical problems such as the large bias of the maximum likelihood estimator continue to hinder the implementation of such methods. This has led to the development of many estimators minimizing bias and, most recently, an unbiased estimator based on sequential binomial sampling. Previous research, however, has focused heavily on the simple case where no misclassification is assumed and only one trait is to be tested. In this talk, we consider the problem of unbiased estimation in these broader areas, giving constructions of such estimators for several cases. We show that, outside of the standard case addressed previously in the literature, it is impossible to find any proper unbiased estimator, that is, an estimator giving only values in the parameter space. This is shown to hold generally under any binomial or multinomial sampling plans.
  
  
• Sample-Size Re-estimation in Two-Stage Bioequivalence Trials

  Speaker: Eric Slud (Dept. of Mathematics (Statistics Program)) -
  

  When: Thu, November 1, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  

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  Abstract: Bioequivalence studies are an essential part of the evaluation of generic drugs. The most common in-vivo bioequivalence (BE) study design is the two-period two-treatment open label crossover design, with a metric of bioavailability such as the log of an approximate integral of the measured concentration of the drug in the blood (log AUC). The observation of interest for each subject is the difference between the measurement in the first and second period of the crossover. When this quantity is assumed approximately normally distributed, the sample size for BE studies using the "Two One-sided Tests" approach is a function of the assumed mean difference, the assumed variance, equivalence margins, type I error rate, and desired power. Since BE studies are often rather small, there is a serious possibility that they are under-powered when the assumed variance turns out to be too small, and it would be preferable to have a blinded study design based on re-estimating the sample-size using only a preliminary estimate of variance calculated without unmasking the treatment labels. However, up to this time there has not been such a two-stage study design guaranteed to maintain experimentwise type I error rate in small samples, apart from inefficient procedures related to Stein's 1945 two-stage procedure.
  In the research described in this talk, expanding on a portion of Meiyu Shen's 2015 UMD thesis, a two-stage sample-size re-estimation design will be presented. The idea, for second-stage sample size expressed as a function of first-stage estimated sample variance, is to calculate the second-stage rejection threshold in such a way that the experimentwise type I error probability maximized over the (unknown) true variance is equal to the prescribed alpha (usually 0.05). This idea is shown to be computationally and practically feasible in the setting of BE studies.
  
  This work is joint with Meiyu Shen and Estelle Russek-Cohen of FDA.
  
  
• Calibrating Dependence between Random Elements

  Speaker: Abram Kagan (UMCP) -
  

  When: Thu, November 8, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  

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  Abstract: Properties of a measure of dependence will be presented that, in my opinion, should be satisfied by any natural measure of dependence.
  
  The main goal is construction of a calibrated scale of dependence between random elements X and Y that is based on the dimension of the range of the projector of the subspace L^{2}(X) of L^{2}(X, Y) into L^{2}(Y).
  
  For independent X, Y the range is one-dimensional and this property is characteristic of independence.
  
• On Reproducibility of Research Findings, Boundary of Meaning and Type S Errors

  Speaker: Prof. Ron Kenett ( KPA Ltd and the Samuel Neaman Institute, Technion, Israel) -
  

  When: Thu, November 29, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  

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  Abstract: The question of reproducibility of research outcomes is discussed now in the open press with a potential negative
  impact on science as a whole. In dealing with this question, from a statistical view point, several methodological
  advances have been proposed (like FDR) and several clarification attempts have been published (like the ASA
  statement on the p value). These attempts seem to only partially address the rising concerns of the public and
  research funding agencies.
  Kenett and Shmueli in Clarifying the terminology that describes scientific reproducibility, Nature Methods, 12(8), p
  699, 2015, review the terminology used in this debate and refer to generalizability, as a dimension that can clarify
  what are research claims that should be scrutinize as reproducible. Generalizability is one of the eight dimensions
  of the information quality (InfoQ) framework presented in Kenett and Shmueli, On information quality: The
  Potential of Data and Analytics to Generate Knowledge, John Wiley and Sons, 2016.
  In this talk, we expand on the idea of generalizability of research findings by referring to Type S errors proposed in
  Gelman and Carlin (2014) [Beyond power calculations: Assessing Type S (sign) and Type M (magnitude) errors,
  Perspectives on Psychological Science, Vol. 9(6), pp. 641–651]. The talk will first discuss methods for setting up a
  boundary of meaning used in generalizing research findings. It will then show how Type S errors and directional
  FDR methods fit with this generalizability approach. An example from research in localized colon cancer
  diagnostics will be used to demonstrate the approach.
  
• Mathematical Aspects of Machine Learning

  Speaker: Wojtek Czaja (Dept. of Mathematics, UMCP) -
  

  When: Thu, December 6, 2018 - 3:30pm
  Where: Kirwan Hall 1313
  

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  Abstract: In recent years machine learning with its focus on predictive and
  generatve abilities of learning algorithms became a focus of attention of
  researchers across many fields, incuding mathematics. In this talk we will
  present some of the aspects of mathematical contributions to machine
  learning, devoting our attention to approximation theory, optimization,
  and convolutional networks.