215 research outputs found
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Influencers and Communities in Social Networks
Integration of social media characteristics into an econometric framework requires modeling a high dimensional dynamic network with dimensions of parameter Θ typically much larger than the number of observations. To cope with this problem we introduce a new structural model which supposes that the network is driven by influencers. We additionally assume the community structure of the network, such that the users from the same community depend on the same influencers. An estimation procedure is proposed based on a greedy algorithm and LASSO. Through theoretical study and simulations, we show that the matrix parameter can be estimated even when the observed time interval is smaller than the size of the network. Using a novel dataset of 1069K messages from 30K users posted on the microblogging platform StockTwits during a 4-year period (01.2014-12.2018) and quantifying their opinions via natural language processing, we model their dynamic opinions network and further separate the network into communities. With a sparsity regularization, we are able to identify important nodes in the network
Mixtures of nonparametric autoregressions
We consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models.We propose nonparametric estimators for the functions characterising the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties. © American Statistical Association and Taylor & Francis 2011.postprin
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Mode-Based Classifier: A Robust and Flexible Discriminant Analysis for High-Dimensional Data
This file available on this institutional repository is a preprint. It has not been certified by peer review. It is freely available at http://www3.stat.sinica.edu.tw/ss_newpaper/SS-2023-0014_na.pdf.Supplementary Materials: In the supplementary materials, we present additional results for simulation examples and real data analysis, and provide the technical results of Theorems 1-3.High-dimensional classification is both challenging and of interest in numerous applications.
Componentwise distance-based classifiers, which utilize partial information with known categories,
such as mean, median and quantiles, provide a convenient way. However, when the input features are
heavy-tailed or contain outliers, performance of the centroid classifier can be poor. Beyond that, it
frequently occurs that a population consists of two or more subpopulations, the mean, median and
quantiles in this scenario fail to capture such a structure that can be instead preserved by mode,
which is an appealing measure of considerable significance but might be neglected. This paper thus
introduces and investigates componentwise mode-based classifiers that can reveal important structures
missed by existing distance-based classifiers. We explore several strategies for defining the family of
mode-based classifiers, including the unimodal classifiers, the multimodal classifier and the quantilemode
classifier. The unimodal classifiers are proposed based on componentwise unimodal distance
and kernel mode estimation, and the multimodal classifier is constructed by identifying all the local
modes of a distribution according to a novel introduced algorithm. We establish the asymptotic
properties of these methods and demonstrate through simulation studies and three real datasets that
the mode-based classifiers compare favorably to the current state-of-art methods.The research of W. Xiong was supported in part by NSFC grants 12001101 and the Fundamental Research
Funds for the Central Universities in UIBE CXTD14-05
A non-linear VAD for noisy environments
This paper deals with non-linear transformations for improving the
performance of an entropy-based voice activity detector (VAD). The idea to use
a non-linear transformation has already been applied in the field of speech
linear prediction, or linear predictive coding (LPC), based on source separation
techniques, where a score function is added to classical equations in order to
take into account the true distribution of the signal. We explore the possibility
of estimating the entropy of frames after calculating its score function, instead
of using original frames. We observe that if the signal is clean, the estimated
entropy is essentially the same; if the signal is noisy, however, the frames
transformed using the score function may give entropy that is different in
voiced frames as compared to nonvoiced ones. Experimental evidence is given
to show that this fact enables voice activity detection under high noise, where
the simple entropy method fails
Prediction of photoperiodic regulators from quantitative gene circuit models
Photoperiod sensors allow physiological adaptation to the changing seasons. The external coincidence hypothesis postulates that a light-responsive regulator is modulated by a circadian rhythm. Sufficient data are available to test this quantitatively in plants, though not yet in animals. In Arabidopsis, the clock-regulated genes CONSTANS (CO) and FLAVIN, KELCH, F-BOX (FKF1) and their lightsensitive proteins are thought to form an external coincidence sensor. We use 40 timeseries of molecular data to model the integration of light and timing information by CO, its target gene FLOWERING LOCUS T (FT), and the circadian clock. Among other predictions, the models show that FKF1 activates FT. We demonstrate experimentally that this effect is independent of the known activation of CO by FKF1, thus we locate a major, novel controller of photoperiodism. External coincidence is part of a complex photoperiod sensor: modelling makes this complexity explicit and may thus contribute to crop improvement
A regularity class for the roots of nonnegative functions
We investigate the regularity of the positive roots of a non-negative
function of one-variable. A modified H\"older space is
introduced such that if then . This provides sufficient conditions to overcome the usual limitation
in the square root case () for H\"older functions that
need be no more than in general. We also derive bounds on the wavelet
coefficients of , which provide a finer understanding of its local
regularity.Comment: 12 page
Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.Progress in olfactory research is currently hampered by incomplete knowledge about chemical receptive ranges of primary receptors. Moreover, the chemical logic underlying the arrangement of computational units in the olfactory bulb has still not been resolved. We undertook a large-scale approach at characterising molecular receptive ranges (MRRs) of glomeruli in the dorsal olfactory bulb (dOB) innervated by the MOR18-2 olfactory receptor, also known as Olfr78, with human ortholog OR51E2. Guided by an iterative approach that combined biological screening and machine learning, we selected 214 odorants to characterise the response of MOR18-2 and its neighbouring glomeruli. We found that a combination of conventional physico-chemical and vibrational molecular descriptors performed best in predicting glomerular responses using nonlinear Support-Vector Regression. We also discovered several previously unknown odorants activating MOR18-2 glomeruli, and obtained detailed MRRs of MOR18-2 glomeruli and their neighbours. Our results confirm earlier findings that demonstrated tunotopy, that is, glomeruli with similar tuning curves tend to be located in spatial proximity in the dOB. In addition, our results indicate chemotopy, that is, a preference for glomeruli with similar physico-chemical MRR descriptions being located in spatial proximity. Together, these findings suggest the existence of a partial chemical map underlying glomerular arrangement in the dOB. Our methodology that combines machine learning and physiological measurements lights the way towards future high-throughput studies to deorphanise and characterise structure-activity relationships in olfaction.Peer reviewe
Non-linear regression models for Approximate Bayesian Computation
Approximate Bayesian inference on the basis of summary statistics is
well-suited to complex problems for which the likelihood is either
mathematically or computationally intractable. However the methods that use
rejection suffer from the curse of dimensionality when the number of summary
statistics is increased. Here we propose a machine-learning approach to the
estimation of the posterior density by introducing two innovations. The new
method fits a nonlinear conditional heteroscedastic regression of the parameter
on the summary statistics, and then adaptively improves estimation using
importance sampling. The new algorithm is compared to the state-of-the-art
approximate Bayesian methods, and achieves considerable reduction of the
computational burden in two examples of inference in statistical genetics and
in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and
Computin
Tune in to your emotions: a robust personalized affective music player
The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application
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