electroencephalogram (eeg) is one of the useful biological signals to distinguish different brain diseases and mental states. in recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from eeg signals. in this research, we introduce an emot...

in the present study, a heterogeneous and homogeneous gas flow dispersion model for simulation and optimization of a large-scale catalytic slurry reactor for the direct synthesis of dimethyl ether (dme) from synthesis gas (syngas) and co2, using a churn-turbulent regime was developed. in the heterogeneous flow model, the gas phase was distributed into two bubble phases including small and large...

We suggest an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are different from observable regression residuals.

Abstract We propose a model to forecast large realized covariance matrices of returns, applying it the constituents S&P 500 daily. To address curse dimensionality, we decompose return matrix using standard firm-level factors (e.g., size, value, and profitability) use sectoral restrictions in residual matrix. This restricted is then estimated vector heterogeneous autoregressive models with l...

Ground-level ozone concentration is a key indicator of air quality. There may exist sudden changes in ozone concentration data over a long time horizon, which may be caused by the implementation of government regulations and policies, such as establishing exhaust emission limits for on-road vehicles. To monitor and assess the efficacy of these policies, we propose a methodology for detecting ch...

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed whe...

The simultaneous analysis of a number of related datasets using a single statistical model is an important problem in statistical computing. A parameterized statistical model is to be fitted on multiple datasets and tested for goodness of fit within a fixed analytical framework. Definitive conclusions are hopefully achieved by analyzing the datasets together. This paper proposes a strategy for ...

We propose the autoregressive HMM for speech synthesis. We show that the autoregressive HMM supports efficient EM parameter estimation and that we can use established effective synthesis techniques such as synthesis considering global variance with minimal modification. The autoregressive HMM uses the same model for parameter estimation and synthesis in a consistent way, in contrast to the stan...

The number of flights is a thing to measure the marketing performance aviation services. Forecasting done so that airlines can make decisions in increasing passengers and revenue. at various airports has relationship between time location. suitable method for forecasting Generalized Space Time Autoregressive (GSTAR) method. GSTAR used series data location heterogeneous characteristics. This stu...

The objective of this study was to compare test-day (TD) models with autoregressive covariance structures for the estimation of genetic and environmental components of variance for milk, fat and protein yields, and somatic cell score (SCS) in Holstein cows. Four models were compared: model I (CS model) was a simple TD repeatability animal model with compound symmetry covariance structure for en...