scholarly journals Estimating Dynamic Signals From Trial Data With Censored Values

2017 ◽  
Vol 1 ◽  
pp. 58-81 ◽  
Author(s):  
Ali Yousefi ◽  
Darin D. Dougherty ◽  
Emad N. Eskandar ◽  
Alik S. Widge ◽  
Uri T. Eden
Keyword(s):  
Censored Data ◽  
Latent Variable ◽  
Statistical Power ◽  
Computationally Efficient ◽  
Behavioral Experiments ◽  
Modeling Framework ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Space Modeling

Censored data occur commonly in trial-structured behavioral experiments and many other forms of longitudinal data. They can lead to severe bias and reduction of statistical power in subsequent analyses. Principled approaches for dealing with censored data, such as data imputation and methods based on the complete data’s likelihood, work well for estimating fixed features of statistical models but have not been extended to dynamic measures, such as serial estimates of an underlying latent variable over time. Here we propose an approach to the censored-data problem for dynamic behavioral signals. We developed a state-space modeling framework with a censored observation process at the trial timescale. We then developed a filter algorithm to compute the posterior distribution of the state process using the available data. We showed that special cases of this framework can incorporate the three most common approaches to censored observations: ignoring trials with censored data, imputing the censored data values, or using the full information available in the data likelihood. Finally, we derived a computationally efficient approximate Gaussian filter that is similar in structure to a Kalman filter, but that efficiently accounts for censored data. We compared the performances of these methods in a simulation study and provide recommendations of approaches to use, based on the expected amount of censored data in an experiment. These new techniques can broadly be applied in many research domains in which censored data interfere with estimation, including survival analysis and other clinical trial applications.

scholarly journals Bayesian Decoder Models with a Discriminative Observation Process

2020 ◽  
Author(s):  
Mohammad R. Rezaei ◽  
Alex E. Hadjinicolaou ◽  
Sydney S. Cash ◽  
Uri T. Eden ◽  
Ali Yousefi
Keyword(s):  
State Space ◽  
Neural Activity ◽  
Model Development ◽  
High Dimensional ◽  
Modeling Framework ◽  
Movement Trajectories ◽  
State Process ◽  
Observation Process ◽  
Space Modeling ◽  
Low Dimensional

AbstractThe Bayesian state-space neural encoder-decoder modeling framework is an established solution to reveal how changes in brain dynamics encode physiological covariates like movement or cognition. Although the framework is increasingly being applied to progress the field of neuroscience, its application to modeling high-dimensional neural data continues to be a challenge. Here, we propose a novel solution that avoids the complexity of encoder models that characterize high-dimensional data as a function of the underlying state processes. We build a discriminative model to estimate state processes as a function of current and previous observations of neural activity. We then develop the filter and parameter estimation solutions for this new class of state-space modeling framework called the “direct decoder” model. We apply the model to decode movement trajectories of a rat in a W-shaped maze from the ensemble spiking activity of place cells and achieve comparable performance to modern decoding solutions, without needing an encoding step in the model development. We further demonstrate how a dynamical auto-encoder can be built using the direct decoder model; here, the underlying state process links the high-dimensional neural activity to the behavioral readout. The dynamical auto-encoder can optimally estimate the low-dimensional dynamical manifold which represents the relationship between brain and behavior.

Filters and parameter estimation for a partially observable system subject to random failure with continuous-range observations

2004 ◽  
Vol 36 (4) ◽  
pp. 1212-1230 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Prediction Problems ◽  
Continuous Range

We consider a failure-prone system operating in continuous time. Condition monitoring is conducted at discrete time epochs. The state of the system is assumed to evolve as a continuous-time Markov process with a finite state space. The observation process with continuous-range values is stochastically related to the state process, which, except for the failure state, is unobservable. Combining the failure information and the condition monitoring information, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Updated parameter estimates are obtained using the expectation-maximization (EM) algorithm. Some practical prediction problems are discussed and finally an illustrative example is given using a real dataset.

Recursive filters for a partially observable system subject to random failure

2003 ◽  
Vol 35 (1) ◽  
pp. 207-227 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Partially Observable System ◽  
Prediction Problems

We consider a failure-prone system which operates in continuous time and is subject to condition monitoring at discrete time epochs. It is assumed that the state of the system evolves as a continuous-time Markov process with a finite state space. The observation process is stochastically related to the state process which is unobservable, except for the failure state. Combining the failure information and the information obtained from condition monitoring, and using the change of measure approach, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Up-dated parameter estimates are obtained using the EM algorithm. Some practical prediction problems are discussed and an illustrative example is given using a real dataset.

Filters and parameter estimation for a partially observable system subject to random failure with continuous-range observations

2004 ◽  
Vol 36 (04) ◽  
pp. 1212-1230 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Prediction Problems ◽  
Continuous Range

We consider a failure-prone system operating in continuous time. Condition monitoring is conducted at discrete time epochs. The state of the system is assumed to evolve as a continuous-time Markov process with a finite state space. The observation process with continuous-range values is stochastically related to the state process, which, except for the failure state, is unobservable. Combining the failure information and the condition monitoring information, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Updated parameter estimates are obtained using the expectation-maximization (EM) algorithm. Some practical prediction problems are discussed and finally an illustrative example is given using a real dataset.

scholarly journals A Modeling Framework for Exploring Sampling and Observation Process Biases in Genome and Phenome-wide Association Studies using Electronic Health Records

2018 ◽  
Author(s):  
Lauren J. Beesley ◽  
Lars G. Fritsche ◽  
Bhramar Mukherjee
Keyword(s):  
Sensitivity Analysis ◽  
Electronic Health Records ◽  
Large Scale ◽  
Association Studies ◽  
Modeling Framework ◽  
Health Records ◽  
Association Analyses ◽  
Observation Process ◽  
Special Cases ◽  
Electronic Health

AbstractLarge-scale agnostic association analyses based on existing observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, particular challenges of non-probability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. In general, the extent of the bias that may be introduced by ignoring these factors is unknown. In this paper, we develop a statistical framework for characterizing the degree of bias expected in association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis type approach, this framework can be used to obtain plausible values for parameters of interest given results obtained from standard naive analysis methods under varying degree of misclassification and sampling biases. We develop an online tool for performing this sensitivity analysis in some special cases that occur frequently. Simulations demonstrate promising properties of the proposed way of characterizing biases. We apply our approach to study bias in genetic association studies using data from the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine.

Recursive filters for a partially observable system subject to random failure

2003 ◽  
Vol 35 (01) ◽  
pp. 207-227 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Partially Observable System ◽  
Prediction Problems

We consider a failure-prone system which operates in continuous time and is subject to condition monitoring at discrete time epochs. It is assumed that the state of the system evolves as a continuous-time Markov process with a finite state space. The observation process is stochastically related to the state process which is unobservable, except for the failure state. Combining the failure information and the information obtained from condition monitoring, and using the change of measure approach, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Up-dated parameter estimates are obtained using the EM algorithm. Some practical prediction problems are discussed and an illustrative example is given using a real dataset.

scholarly journals Family Processes, Parenting Practices, and Psychosocial Maturity of Chinese Youths: A Latent Variable Interaction and Mediation Analysis

2021 ◽  
Vol 18 (8) ◽  
pp. 4357
Author(s):  
Jerf W. K. Yeung
Keyword(s):  
Youth Development ◽  
Parenting Practices ◽  
Latent Variable ◽  
Well Being ◽  
Family Processes ◽  
Mediating Effect ◽  
Authoritative Parenting ◽  
Family Socialization ◽  
Modeling Framework ◽  
Psychosocial Maturity

Development of psychosocial maturity has profound implications for youths’ well-being and positive development in the long run. Nevertheless, little research has investigated the way family socialization contributes to youths’ psychosocial maturity. Both the concepts of family socialization and psychosocial maturity are multifaceted and latent, which may lead to biased results if studied by manifest variables. Also, no existing research has discovered how different family socialization components interact latently to contribute to youths’ psychosocial maturity. The current study, based on a sample of 533 Chinese parent-youth dyads, examined the effects of family socialization by positive family processes and authoritative parenting, and their latent interaction in an integrated moderation and mediation modeling framework on Chinese youths’ psychosocial maturity. Results showed that both positive family processes and authoritative parenting, and their latent interaction significantly predicted the higher psychosocial maturity of Chinese youths. Authoritative parenting acted as a mediator for the relationship between positive family processes and Chinese youths’ psychosocial maturity. Furthermore, the mediating effect of authoritative parenting was conditioned by different contexts of positive family processes, the strongest and least strong effects found in high and low positive family processes, respectively, and moderate effect observed in medium positive family processes. Findings of the current study contribute to our understanding of the complicated family mechanism in relation to youth development, especially in this digital era.

Geometric, Spatial Path Tracking Using Non-Redundant Manipulators via Speed-Ratio Control

2010 ◽  
Author(s):  
Satyajit Ambike ◽  
James P. Schmiedeler ◽  
Michael M. Stanisˇic´
Keyword(s):  
Path Tracking ◽  
Speed Ratio ◽  
Redundant Manipulators ◽  
Computationally Efficient ◽  
Third Order ◽  
Practical Advantage ◽  
First Order ◽  
Canonical Frame ◽  
Special Cases ◽  
Six Degree Of Freedom

Path tracking can be accomplished by separating the control of the desired trajectory geometry and the control of the path variable. Existing methods accomplish tracking of up to third-order geometric properties of planar paths and up to second-order properties of spatial paths using non-redundant manipulators, but only in special cases. This paper presents a novel methodology that enables the geometric tracking of a desired planar or spatial path to any order with any non-redundant regional manipulator. The governing first-order coordination equation for a spatial path-tracking problem is developed, the repeated differentiation of which generates the coordination equation of the desired order. In contrast to previous work, the equations are developed in a fixed global frame rather than a configuration-dependent canonical frame, providing a significant practical advantage. The equations are shown to be linear, and therefore, computationally efficient. As an example, the results are applied to a spatial 3-revolute mechanism that tracks a spatial path. Spatial, rigid-body guidance is achieved by applying the technique to three points on the end-effector of a six degree-of-freedom robot. A spatial 6-revolute robot is used as an illustration.

scholarly journals Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

2019 ◽  
Author(s):  
John A. Lees ◽  
T. Tien Mai ◽  
Marco Galardini ◽  
Nicole E. Wheeler ◽  
Jukka Corander
Keyword(s):  
Genetic Variants ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Bacterial Species ◽  
False Positive Rate ◽  
Model Fitting ◽  
Joint Modeling ◽  
Inference Procedure ◽  
Modeling Framework ◽  
Narrow Sense Heritability

ABSTRACTDiscovery of influential genetic variants and prediction of phenotypes such as antibiotic resistance are becoming routine tasks in bacterial genomics. Genome-wide association study (GWAS) methods can be applied to study bacterial populations, with a particular emphasis on alignment-free approaches, which are necessitated by the more plastic nature of bacterial genomes. Here we advance bacterial GWAS by introducing a computationally scalable joint modeling framework, where genetic variants covering the entire pangenome are compactly represented by unitigs, and the model fitting is achieved using elastic net penalization. In contrast to current leading GWAS approaches, which test each genotype-phenotype association separately for each variant, our joint modelling approach is shown to lead to increased statistical power while maintaining control of the false positive rate. Our inference procedure also delivers an estimate of the narrow-sense heritability, which is gaining considerable interest in studies of bacteria. Using an extensive set of state-of-the-art bacterial population genomic datasets we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. We expect that these advances will pave the way for the next generation of high-powered association and prediction studies for an increasing number of bacterial species.

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