Parametric statistics ≈ Parametric statistics
View article: Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates Open
Significance Functional MRI (fMRI) is 25 years old, yet surprisingly its most common statistical methods have not been validated using real data. Here, we used resting-state fMRI data from 499 healthy controls to conduct 3 million task gro…
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WaveNet: A Generative Model for Raw Audio Open
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonethel…
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Descriptive statistics and normality tests for statistical data Open
Descriptive statistics are an important part of biomedical research which is used to describe the basic features of the data in the study. They provide simple summaries about the sample and the measures. Measures of the central tendency an…
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rptR: repeatability estimation and variance decomposition by generalized linear mixed‐effects models Open
Summary Intra‐class correlations ( ICC ) and repeatabilities ( R ) are fundamental statistics for quantifying the reproducibility of measurements and for understanding the structure of biological variation. Linear mixed effects models offe…
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Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery Open
In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a f…
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Fourier Neural Operator for Parametric Partial Differential Equations Open
The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For p…
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Central limit theorem: the cornerstone of modern statistics Open
According to the central limit theorem, the means of a random sample of size, n, from a population with mean, µ, and variance, σ2, distribute normally with mean, µ, and variance, [Formula: see text]. Using the central limit theorem, a vari…
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metan: An R package for multi‐environment trial analysis Open
Multi‐environment trials (MET) are crucial steps in plant breeding programs that aim at increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data man…
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The power of outliers (and why researchers should ALWAYS check for them) Open
There has been much debate in the literature regarding what to do with extreme or influential data points. The goal of this paper is to summarize the various potential causes of extreme scores in a data set (e.g., data recording or entry e…
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FMRI Clustering in AFNI: False-Positive Rates Redux Open
Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 2016 have become a large topic within (and outside) neuroimaging. They concluded that existing parametric methods for determini…
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Time-varying covariates and coefficients in Cox regression models Open
Time-varying covariance occurs when a covariate changes over time during the follow-up period. Such variable can be analyzed with the Cox regression model to estimate its effect on survival time. For this it is essential to organize the da…
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FastSpeech: Fast, Robust and Controllable Text to Speech Open
Neural network based end-to-end text to speech (TTS) has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron 2) usually first generate mel-spectrogram from text, and then synthesize speech from the m…
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Learning the solution operator of parametric partial differential equations with physics-informed DeepONets Open
Enabling the rapid emulation of parametric differential equations with physics-informed deep operator networks.
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Non-intrusive reduced order modeling of nonlinear problems using neural networks Open
We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decompositio…
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DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks Open
Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, medicine and many other areas. A fundamental problem is to understand the relationship between the covariates and the (distribution of) surviv…
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Ultra-low-loss on-chip resonators with sub-milliwatt parametric oscillation threshold Open
On-chip optical resonators have the promise of revolutionizing numerous fields including metrology and sensing; however, their optical losses have always lagged behind their larger discrete resonator counterparts based on crystalline mater…
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Modelling Palaeoecological Time Series Using Generalised Additive Models Open
In the absence of annual laminations, time series generated from lake sediments or other similar stratigraphic sequences are irregularly spaced in time, which complicates formal analysis using classical statistical time series models. In l…
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How to Measure Galaxy Star Formation Histories. II. Nonparametric Models Open
Nonparametric star formation histories (SFHs) have long promised to be the “gold standard” for galaxy spectral energy distribution (SED) modeling as they are flexible enough to describe the full diversity of SFH shapes, whereas parametric …
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DreamFusion: Text-to-3D using 2D Diffusion Open
Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient archit…
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Machine learning methods for crop yield prediction and climate change impact assessment in agriculture Open
Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield modeling that uses a semiparametric variant…
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The distance function effect on k-nearest neighbor classification for medical datasets Open
In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the…
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A general approach for predicting the behavior of the Supreme Court of the United States Open
Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To …
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Basic statistical tools in research and data analysis Open
Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningle…
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12-Photon Entanglement and Scalable Scattershot Boson Sampling with Optimal Entangled-Photon Pairs from Parametric Down-Conversion Open
Entangled-photon sources with simultaneously near-unity heralding efficiency and indistinguishability are the fundamental elements for scalable photonic quantum technologies. We design and realize a degenerate telecommunication wavelength …
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Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications Open
Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. Though these methods co…
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Noisy Networks for Exploration Open
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. The parameters of the noise are …
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Advanced Bayesian Multilevel Modeling with the R Package brms Open
The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models, which are fitted with the probabilistic programming language Stan behind the scenes. Several response distributions are supporte…
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Analysis of Finite-Control-Set Model Predictive Current Control With Model Parameter Mismatch in a Three-Phase Inverter Open
It is well known that predictive control methods can be affected by the presence of modeling errors. The extent to which finite-control-set model predictive control (FCS-MPC) is influenced by parametric uncertainties is a recurrent concern…
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A Fast Dense Spectral–Spatial Convolution Network Framework for Hyperspectral Images Classification Open
Recent research shows that deep-learning-derived methods based on a deep convolutional neural network have high accuracy when applied to hyperspectral image (HSI) classification, but long training times. To reduce the training time and imp…
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Rethinking Semantic Segmentation: A Prototype View Open
sponsorship: This work was supported by CCF-Baidu Open Fund and ARC DECRA DE220101390 (CCF-Baidu Open Fund, ARC DECRA|DE220101390, Australian Research Council|DE220101390)