165 research outputs found
Robust and Unbiased Variance of GLM Coefficients for Misspecified Autocorrelation and Hemodynamic Response Models in fMRI
As a consequence of misspecification of the hemodynamic response and noise variance models, tests on general linear model coe cients are not valid. Robust estimation of the variance of the general linear model (GLM) coecients in fMRI time series is therefore essential. In this paper an alternative method to estimate the variance of the GLM coe cients accurately is suggested and compared to other methods. The alternative, referred to as the sandwich, is based primarily on the fact that the time series are obtained from multiple exchangeable stimulus presentations. The analytic results show that the sandwich is unbiased. Using this result, it is possible to obtain an exact statistic which keeps the 5% false positive rate. Extensive Monte Carlo simulations show that the sandwich is robust against misspeci cation of the autocorrelations and of the hemodynamic response model. The sandwich is seen to be in many circumstances robust, computationally efficient, and flexible with respect to correlation structures across the brain. In contrast, the smoothing approach can be robust to a certain extent but only with specific knowledge of the circumstances for the smoothing parameter
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data
We present the R-package mgm for the estimation of k-order Mixed Graphical
Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional
data. These are a useful extensions of graphical models for only one variable
type, since data sets consisting of mixed types of variables (continuous,
count, categorical) are ubiquitous. In addition, we allow to relax the
stationarity assumption of both models by introducing time-varying versions
MGMs and mVAR models based on a kernel weighting approach. Time-varying models
offer a rich description of temporally evolving systems and allow to identify
external influences on the model structure such as the impact of interventions.
We provide the background of all implemented methods and provide fully
reproducible examples that illustrate how to use the package
A Tutorial on Estimating Time-Varying Vector Autoregressive Models
Time series of individual subjects have become a common data type in
psychological research. These data allow one to estimate models of
within-subject dynamics, and thereby avoid the notorious problem of making
within-subjects inferences from between-subjects data, and naturally address
heterogeneity between subjects. A popular model for these data is the Vector
Autoregressive (VAR) model, in which each variable is predicted as a linear
function of all variables at previous time points. A key assumption of this
model is that its parameters are constant (or stationary) across time. However,
in many areas of psychological research time-varying parameters are plausible
or even the subject of study. In this tutorial paper, we introduce methods to
estimate time-varying VAR models based on splines and kernel-smoothing
with/without regularization. We use simulations to evaluate the relative
performance of all methods in scenarios typical in applied research, and
discuss their strengths and weaknesses. Finally, we provide a step-by-step
tutorial showing how to apply the discussed methods to an openly available time
series of mood-related measurements
Logistic regression and Ising networks: prediction and estimation when violating lasso assumptions
The Ising model was originally developed to model magnetisation of solids in
statistical physics. As a network of binary variables with the probability of
becoming 'active' depending only on direct neighbours, the Ising model appears
appropriate for many other processes. For instance, it was recently applied in
psychology to model co-occurrences of mental disorders. It has been shown that
the connections between the variables (nodes) in the Ising network can be
estimated with a series of logistic regressions. This naturally leads to
questions of how well such a model predicts new observations and how well
parameters of the Ising model can be estimated using logistic regressions. Here
we focus on the high-dimensional setting with more parameters than observations
and consider violations of assumptions of the lasso. In particular, we
determine the consequences for both prediction and estimation when the sparsity
and restricted eigenvalue assumptions are not satisfied. We explain by using
the idea of connected copies (extreme multicollinearity) the fact that
prediction becomes better when either sparsity or multicollinearity is not
satisfied. We illustrate these results with simulations.Comment: to appear, Behaviormetrika, 201
Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder
Mental disorders like major depressive disorder can be seen as complex
dynamical systems. In this study we investigate the dynamic behaviour of
individuals to see whether or not we can expect a transition to another mood
state. We introduce a mean field model to a binomial process, where we reduce a
dynamic multidimensional system (stochastic cellular automaton) to a
one-dimensional system to analyse the dynamics. Using maximum likelihood
estimation, we can estimate the parameter of interest which, in combination
with a bifurcation diagram, reflects the expectancy that someone has to
transition to another mood state. After validating the proposed method with
simulated data, we apply this method to two empirical examples, where we show
its use in a clinical sample consisting of patients diagnosed with major
depressive disorder, and a general population sample. Results showed that the
majority of the clinical sample was categorized as having an expectancy for a
transition, while the majority of the general population sample did not have
this expectancy. We conclude that the mean field model has great potential in
assessing the expectancy for a transition between mood states. With some
extensions it could, in the future, aid clinical therapists in the treatment of
depressed patients.Comment: arXiv admin note: text overlap with arXiv:1610.0504
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data
Previous research on EMA data of mental disorders was mainly focused on
multivariate regression-based approaches modeling each individual separately.
This paper goes a step further towards exploring the use of non-linear
interpretable machine learning (ML) models in classification problems. ML
models can enhance the ability to accurately predict the occurrence of
different behaviors by recognizing complicated patterns between variables in
data. To evaluate this, the performance of various ensembles of trees are
compared to linear models using imbalanced synthetic and real-world datasets.
After examining the distributions of AUC scores in all cases, non-linear models
appear to be superior to baseline linear models. Moreover, apart from
personalized approaches, group-level prediction models are also likely to offer
an enhanced performance. According to this, two different nomothetic approaches
to integrate data of more than one individuals are examined, one using directly
all data during training and one based on knowledge distillation.
Interestingly, it is observed that in one of the two real-world datasets,
knowledge distillation method achieves improved AUC scores (mean relative
change of +17\% compared to personalized) showing how it can benefit EMA data
classification and performance.Comment: 13 pages, 2 figures, accepted on the symposium 'Intelligent Data
Analysis' (2022
Interpreting the Ising Model: The Input Matters
The Ising model is a model for pairwise interactions between binary variables
that has become popular in the psychological sciences. It has been first
introduced as a theoretical model for the alignment between positive (+1) and
negative (-1) atom spins. In many psychological applications, however, the
Ising model is defined on the domain instead of the classical domain
. While it is possible to transform the parameters of a given Ising
model in one domain to obtain a statistically equivalent model in the other
domain, the parameters in the two versions of the Ising model lend themselves
to different interpretations and imply different dynamics, when studying the
Ising model as a dynamical system. In this tutorial paper, we provide an
accessible discussion of the interpretation of threshold and interaction
parameters in the two domains and show how the dynamics of the Ising model
depends on the choice of domain. Finally, we provide a transformation that
allows to transform the parameters in an Ising model in one domain into a
statistically equivalent Ising model in the other domain
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