Structural Time Series Models with Feedback Mechanisms

Structural time series models have applications in many different fields such as biology, economics, and meteorology. A structural time series model can be represented as a state-space model where the states of the system represent the unobserved components and the structural parameters have clear interpretations. This paper introduces a class of structural time series models that incorporate feedback from the latent components of the history. An iterative procedure is proposed for estimation. These models allow flexible and robust feedback mechanisms, have clear interpretations, and have a computationally efficient estimation procedure. They are applied to hormone data to characterize hormone secretion and to explore a potential feedback mechanism.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65683/1/j.0006-341X.2000.00686.x.pd

A Bayesian Approach to Modeling Associations Between Pulsatile Hormones

Many hormones are secreted in pulses. The pulsatile relationship between hormones regulates many biological processes. To understand endocrine system regulation, time series of hormone concentrations are collected. The goal is to characterize pulsatile patterns and associations between hormones. Currently each hormone on each subject is fitted univariately. This leads to estimates of the number of pulses and estimates of the amount of hormone secreted; however, when the signal-to-noise ratio is small, pulse detection and parameter estimation remains difficult with existing approaches. In this article, we present a bivariate deconvolution model of pulsatile hormone data focusing on incorporating pulsatile associations. Through simulation, we exhibit that using the underlying pulsatile association between two hormones improves the estimation of the number of pulses and the other parameters defining each hormone. We develop the one-to-one, driver–response case and show how birth–death Markov chain Monte Carlo can be used for estimation. We exhibit these features through a simulation study and apply the method to luteinizing and follicle stimulating hormones.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65251/1/j.1541-0420.2008.01117.x.pd

Analysis of Pulsatile Hormone Concentration Profiles with Nonconstant Basal Concentration: A Bayesian Approach

Many challenges arise in the analysis of pulsatile, or episodic, hormone concentration time series data. Among these challenges is the determination of the number and location of pulsatile events and the discrimination of events from noise. Analyses of these data are typically performed in two stages. In the first stage, the number and approximate location of the pulses are determined. In the second stage, a model (typically a deconvolution model) is fit to the data conditional on the number of pulses. Any error made in the first stage is carried over to the second stage. Furthermore, current methods, except two, assume that the underlying basal concentration is constant. We present a fully Bayesian deconvolution model that simultaneously estimates the number of secretion episodes, as well as their locations, and a nonconstant basal concentration. This model obviates the need to determine the number of events a priori. Furthermore, we estimate probabilities for all “candidate” event locations. We demonstrate our method on a real data set.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65796/1/j.1541-0420.2007.00809.x.pd

TalkingBoogie: Collaborative Mobile AAC System for Non-verbal Children with Developmental Disabilities and Their Caregivers

Augmentative and alternative communication (AAC) technologies are widely used to help non-verbal children enable communication. For AAC-aided communication to be successful, caregivers should support children with consistent intervention strategies in various settings. As such, caregivers need to continuously observe and discuss children's AAC usage to create a shared understanding of these strategies. However, caregivers often find it challenging to effectively collaborate with one another due to a lack of family involvement and the unstructured process of collaboration. To address these issues, we present TalkingBoogie, which consists of two mobile apps: TalkingBoogie-AAC for caregiver-child communication, and TalkingBoogie-coach supporting caregiver collaboration. Working together, these applications provide contextualized layouts for symbol arrangement, scaffold the process of sharing and discussing observations, and induce caregivers' balanced participation. A two-week deployment study with four groups (N=11) found that TalkingBoogie helped increase mutual understanding of strategies and encourage balanced participation between caregivers with reduced cognitive loads.SNU Undergraduate Research Program through the Faculty of Liberal Education, Seoul National University (2019-23) National Research Foundation of Korea Grant funded by the Korean Government (NRF-2019S1A5A2A01045980

Models And Methods For Hormone Pulse Analysis.

A hormone pulse is a release of hormone from a gland into the blood of an organism. This event causes a relatively rapid rise, followed by a somewhat slower decline, in the serum concentration of the hormone. These fluctuations in concentration are involved in regulating basic physiological processes, such as growth and reproduction. Variations in the sizes and timing of the pulsatile release events are of interest in understanding the functioning of these regulatory mechanisms. The data required to study hormone pulse patterns are obtained in pulse bleed experiments, in which blood samples are taken from a common site of a single organism at regular time intervals and assayed for the concentrations of one or more hormones. A statistical pulse detection method is designed to estimate the timing and sizes of the pulses in such a data series. Several methods are currently in use, including those of Goodman and Karsch (1980), Van Cauter (1981), Merriam and Wachter (1982), and Clifton and Steiner (1983). In this thesis, assumptions about the nature of the biological processes involved in pulsatile hormone release are discussed, and a first-order differential equation describing the variation of hormone concentration over time is derived. A discrete time version of this equation leads to a parametric model for hormone pulse data. Based on this model the existing pulse detection methods are critiqued, and a new pulse detection method is developed. The new method involves fitting the proposed parametric model to a pulse bleed data set by alternating between (a) nonlinear parameter estimation while holding fixed the times at which pulses are estimated to occur, and (b) stepwise addition and deletion of pulse times while holding the nonlinear parameter(s) fixed. Techniques for generating simulated hormone pulse data are developed, and criteria for assessing pulse detection methods are discussed. The results of simulation experiments comparing the new method with the methods referenced above are then presented. Finally, examples of the application of the various pulse detection methods to data from actual pulse bleed experiments are presented and discussed.Ph.D.Biological SciencesBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/128023/2/8712156.pd

The reliability of DSM impact estimates

Demand-side management (DSM) critics continue to question the reliability of DSM program savings, and therefore, the need for funding such programs. In this paper, the authors examine the issues underlying the discussion of reliability of DSM program savings (e.g., bias and precision) and compare the levels of precision of DSM impact estimates for three utilities. Overall, the precision results from all three companies appear quite similar and, for the most part, demonstrate reasonably good precision levels around DSM savings estimate. The conclude by recommending activities for program managers and evaluators for increasing the understanding of the factors leading to DSM uncertainty and for reducing the level of DSM uncertainty

Energy Efficiency and Electric System Reliability

OAK B188 Energy Efficiency and Electric System Reliabilit

Richland isotope production matrix

Isotope production calculations have been prepared in the past to indicate present and potential reactor capability; however, the cases evaluated generally considered production of only one or possibly two isotopes. With the wide variety and potential demand for numerous isotopes, the question arises as to what the production capability would be if several isotopes were being produced at the same time. To study this problem in more detail and determine which of several alternates may be used in meeting production requirements, the Case Generator and Evaluator System has been developed. This document presents a production matrix and reviews the assumptions used to derive the matrix. This matrix should be useful in planning future CAGE isotope cases