Nancy Heckman

 
Prospective Graduate Students / Postdocs

This faculty member is currently not actively recruiting graduate students or Postdoctoral Fellows, but might consider co-supervision together with another faculty member.

Professor

Research Classification

Statistics and Probabilities

Research Interests

functional data analysis
smoothing
splines

Relevant Degree Programs

 

Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - May 2019)
Switching nonparametric regression models (2013)

In this thesis, we propose a methodology to analyze data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z, also called a hidden or latent state. The states form a stochastic process and their possible values are j=1,...,J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. In a Bayesian switching nonparametric regression model the uncertainty about the functions is formulated by modeling the functions as realizations of stochastic processes. In a frequentist switching nonparametric regression model the functions are merely assumed to be smooth. We consider two different data structures: one with N replicates and the other with one single realization. For the hidden states, we consider those that are independent and identically distributed and those that follow a Markov structure. We develop an EM algorithm to estimate the parameters of the latent state process and the functions corresponding to the J states. Standard errors for the parameter estimates of the state process are also obtained. We investigate the frequentist properties of the proposed estimates via simulation studies. Two different applications of the proposed methodology are presented. In the first application we analyze the well-known motorcycle data in an innovative way: treating the data as coming from J>1 simulated accident runs with unobserved run labels. In the second application we analyze daytime power usage on business days in a building treating each day as a replicate and modeling power usage as arising from two functions, one function giving power usage when the cooling system of the building is off, the other function giving power usage when the cooling system is on.

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Master's Student Supervision (2010 - 2018)
Instantaneous dynamics of functional data (2016)

Time dynamic systems can be used in many applications to data modeling. In the case of longitudinal data, the dynamics of the underlying differential equation can often be inferred under minimal assumptions via smoothing based procedures. This is in contrast to the common technique of assuming a prespecified differential equation, and estimating it's parameters. In many cases, one wants to learn the dynamics of a differential equation that incorporates more than just one stochastic process. In the following, we propose extensions to existing two-step smoothing methods that allow for the presence of additional functional data arising from a second stochastic process. We further introduce model comparison techniques to assess the hypothesis that there is a significant change in fit provided by this additional process. These techniques are applied to the instantaneous dynamics of mouse growth data and allow us to make comparisons between mice who have been assigned different genetic and physical conditions. Finally, to study the statistical properties of our proposed techniques, we carry out a simulation study based on the mouse growth data. Supplementary material : http://hdl.handle.net/2429/59574

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Kernel estimation of the drift coefficient of a diffusion process in the presence of measurement error (2014)

Diffusion processes, a class of continuous-time stochastic processes, can be used to model time-series data observed at discrete time points. A diffusion process can be completely characterized by two functions, called the drift coefficient and the diffusion coefficient. For the nonparametric estimation of these two functions, Bandi and Phillips (2003) proved consistency and asymptotic normality of Nadaraya-Watson kernel estimators of the drift and the diffusion coefficient.In some cases, we observe the time-series data with measurement error. For instance, it is a well-known fact that we observe the financial time-series data with measurement errors (Zhou, 1996). For the nonparametric estimation of the drift and the diffusion coefficients in the presence of measurement error, some works are done for the estimation of integrated volatility, which is the integral of the diffusion coefficient over a fixed period of time, but little work exists on the estimation of the drift and the diffusion coefficients themselves. In this thesis, we focus on the estimation of the drift coefficient, and we propose a consistent and asymptotically normal Nadaraya-Watson type kernel estimator of the drift coefficient in the presence of measurement error.

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Dynamic duration of load models (2011)

The duration of load effect is a distinctive and important characteristic of wood strength. It refers to the fact that wood products can usually sustain a high load for a short time but the products may deteriorate and break in the long run. Modelling the duration of load effect and testing wood for specific properties of this effect are important in formulating wood construction standards.Damage accumulation models have been proposed by authors to model the duration of load effects. The models assume that damage is accumulated over time according to the load history, and once the accumulated damage reaches a threshold value, the board will break. Different authors have designed different experiments and proposed different methods for estimating the model parameters. In this work, we consider several damage accumulation models, with a focus on the U.S. model. We investigate the effects of the distributional assumptions for the models, and propose several methods to estimate parameters in the models. Our proposed methods are evaluated via simulation studies. Two real datasets are present for illustration.

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