Bayesian estimation of restricted latent class models: Extending priors, link functions, and structural models

Restricted latent class models (RLCMs) provide a pivotal framework for supporting diagnostic research that enhances human development and opportunities. In earlier research, the focus was on confirmatory methods that required a pre-specified expert-attribute mapping known as a Q matrix. Recent research directions have led to the creation of exploratory methodology that is able to infer the Q matrix without expert intervention. Within this thesis, we seek to extend and improve upon existing exploratory techniques and applications. We begin by developing novel Bayesian methodology that uses a less restrictive monotonicity condition when estimating the underlying latent structure and attributes. Under the formulation, we make further enhancements by extending the framework to the logit-link function through the Pólya-Gamma distribution. Moreover, we determine different regularization approaches that can be applied to the latent structure to induce sparsity. Next, we propose an extension that seeks to address the dependency structure found among attributes. The dependency structure is able to be described by using a higher-order structure for attributes. Estimating the higher-order structure is done by applying techniques from exploratory factor analysis (EFA). Moreover, the latent structure grows exponentially as the number of attributes increases and we provide an option to specify a subset of the latent structure. Another important consideration is there may be more than one strategy that can be used to achieve success on some tasks. We develop new methods for inferring multiple strategies in the presence of expert knowledge. Lastly, we discuss software implementations of the aforementioned methodological developments. Providing implementations lowers the barrier of entry to employing the methods within psychometric community.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste

An inertial sensor calibration platform to estimate and select error models

A new open-source software platform that, among others, allows to select models for inertial sensor stochastic calibration is presented in this paper. This platform consists in a package included in the statistical software R. The identification of stochastic models and estimation of model parameters is based on the method of Generalized Method of Wavelet Moments. This approach provides an extremely general framework for the identification, estimation and testing of models to describe and predict the error signals coming from inertial sensors. With the possibility of estimating complex models made of the sum of different underlying processes, this paper also presents the method with which a model, or a restrict set of models, can be selected that best describes and predicts the error signal

Discussion on maximum likelihood-based methods for inertial sensor calibration

This letter highlights some issues which were overlooked in a recently published paper called “Maximum Likelihood Identification of Inertial Sensor Noise Model Parameters”. The latter paper does not consider existing alternative methods which specifically tackle this issue in a possibly more direct manner and, although remaining a generally valid proposal, does not appear to improve on the earlier proposals. Finally, a simulation study rectifies the poor results of an estimator of reference in the same publication

A computationally efficient platform for inertial sensor calibration

This paper presents the new open-source statistical software package for inertial sensor calibration. This platform is based on the Generalized Method of Wavelet Moments that was recently proposed to estimate simple and composite stochastic models that are typically used in sensor calibration. As opposed to existing techniques, this new package allows to easily and efficiently visualize, estimate and test a wide range of stochastic models that are used to describe and predict the error signals coming from accelerometers and gyroscopes that characterize inertial sensors. The availability of this new tool is of considerable importance since, given the growing use of low-cost IMUs, correctly identifying and precisely estimating the models for the error signals of these sensors allows to greatly improve their navigation accuracy

Extending <i>R</i> with C++: A Brief Introduction to Rcpp

<p>R has always provided an application programming interface (API) for extensions. Based on the <i>C</i> language, it uses a number of macros and other low-level constructs to exchange data structures between the R process and any dynamically loaded component modules authors added to it. With the introduction of the <i>Rcpp</i> package, and its later refinements, this process has become considerably easier yet also more robust. By now, <i>Rcpp</i> has become the most popular extension mechanism for R. This article introduces <i>Rcpp</i>, and illustrates with several examples how the <i>Rcpp Attributes</i> mechanism in particular eases the transition of objects between R and C++ code. Supplementary materials for this article are available online.</p

A computationally efficient framework for automatic inertial sensor calibration

The calibration of (low-cost) inertial sensors has become increasingly important over the past years since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3D-animation. However, this calibration procedure is often quite problematic since the signals issued from these sensors have a complex spectral structure and the methods available to estimate the parameters of these models are either unstable, computationally intensive and/or statistically inconsistent. This paper presents a new software platform for inertial sensor calibration based on the Generalized Method of Wavelet Moments which provides a computationally efficient, flexible, user-friendly and statistically sound tool to estimate and select from a wide range of complex models. The software is developed within the open-source statistical software R and is based on C++ language allowing it to achieve high computational performance

An overview of a new sensor calibration platform

Inertial sensors are increasingly being employed in different types of applications. The reduced cost and the extremely small size makes them the number-one-choice in miniature embedded devices like phones, watches, and small unmanned aerial vehicles. The more complex the application, the more it is necessary to understand the structure of the error signal coming from these sensors. Indeed, their error signals are composed of deterministic and stochastic parts. The deterministic errors or faults can be compensated by proper calibration while the stochastic signal is usually ignored since its modeling is relatively difficult due to computational or statistical reasons, especially due to its complex spectral structure. However, a recently proposed approach called the Generalized Method of Wavelet Moments overcomes these limitations and this paper presents the software platform that implements this method for the analysis of the stochastic errors. As an example throughout the paper we will consider an inertial measurement unit, but the platform can be used for the stochastic calibration of any kind of sensor. The software is developed in the widely used statistical tool R using C++ language. The tools enable the user to study with ease any signal by the means of a vast range of predefined models and tools