Inference for autoregressive and moving average models with extreme value distribution via simulation study by Samuel, Bako Sunday

“…To achieve our objectives, a stationary autoregressive and moving average models with Gumbel distributed innovation is proposed and we characterise the short-term dependence among maxima, arising from light-tailed Gumbel distribution over a range of sample sizes with varying degrees of dependence. …”
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Environmental risk factors for self-harm during imprisonment: A pilot prospective cohort study by Stephenson, T, Harris, I, Armstrong, C, Fazel, S, Short, R, Blackwood, N

Environmental risk factors for self-harm during imprisonment: a prospective cohort study by Stephenson, T, Harris, I, Armstrong, C, Fazel, S, Short, R, Blackwood, N

Multiscale approach to nematic liquid crystals via statistical field theory by Lu, Bing-Sui

“…We propose an approach to a multiscale problem in the theory of thermotropic uniaxial nematics based on the method of statistical field theory. This approach enables us to relate the coefficients A, B, C, L1, and L2 of the Landau-de Gennes free energy for the isotropic-nematic phase transition to the parameters of a molecular model of uniaxial nematics, which we take to be a lattice gas model of nematogenic molecules interacting via a short-ranged potential. …”
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Small sample sizes reduce the replicability of task-based fMRI studies by Turner, Benjamin O., Paul, Erick J., Miller, Michael B., Barbey, Aron K.

“…We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. …”
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Modeling and forecasting customer demands by See, Tian Pau.

“…In this paper, I have made an attempt to seek what statistical models and forecasting techniques which are appropriate to support decision making in the operational level of supply chain while dealing with customer demands on short life cycle products to avoid undesired production condition. …”
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Uncertainty quantification framework for combined statistical spatial downscaling and temporal disaggregation for climate change impact studies on hydrology by Rajendran Queen Suraajini

“…The Statistical Downscaling Model (SDM) is the bridging model which is used to downscale the output from the General Circulation Model (GCM) for increasing the spatial resolution of future climate scenarios. …”
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A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics by Barthel Sorensen, B., Charalampopoulos, A., Zhang, S., Harrop, B. E., Leung, L. R., Sapsis, T. P.

“…Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. …”
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Bayesian neural network language modeling for speech recognition by Xue, Boyang, Hu, Shoukang, Xu, Junhao, Geng, Mengzhe, Liu, Xunying, Meng, Helen

“…State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. …”
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Topological data analysis for fake news detection by Deng, Ran

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Differentially private deep learning for time series data by Dwitami, Inggriany

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Measuring dynamical uncertainty with Revealed Dynamics Markov Models by Bramson, Aaron, Baland, Adrien, Iriki, Atsushi

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Modelling and characterization of membrane fouling in osmotically-driven membrane processes (ODMPs) by Lai, Li

“…From qualitive analysis to quantitative analysis, the location of peak raised by colloidal deposition is used to differentiate the deposition on surface of active layer and inside of support layer after being analyzed by arrival time shift and confirmed by statistical testing. Moreover, the further short-time Fourier transformation (STFT) was applied to transfer the original time domain data into time-frequency domain, which reveal a noticeable magnitude reduction on high frequency components when Rayleigh scattering was triggered by particle inside of membrane pores. …”
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新加坡华语句末语气词 ：在语料库基础上与普通话的对比统计 = Statistical comparison of Singaporean and Mainland Mandarin Chinese : a corpus-based study on sentence final particles... by 白欣彦 Pak, Xin Yan

“…Han et al. 2013; Appre 2008). The statistical results suggest that some variables serve as a differentiating factor among the five SFPs (either in SMC or in MMC). …”
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Forecasting of hydropower production using Box-Jenkins model at Tasik Kenyir, Terengganu by Nornabila, Abu, Nurul Nadrah Aqilah, Tukimat, Noratikah, Abu

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Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model by Abang Mohammad Hudzaifah Abang Shakawi, Ani Shabri, Ruhana Hassan

“…Using data from 1949 to 2016, GM (1,1) was found to be the most suitable model for the given dataset, exhibiting the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as compared to other statistical models such as Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Exponential Smoothing. …”
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An analysis on the traffic processing efficiency of a combination of serial and parallel bottlenecks by Quek, Wei-Liang, Chung, Ning Ning, Chew, Lock Yue

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Machine learning baseline energy model (MLBEM) to evaluate prediction performances in building energy consumption by Mustapa, Rijalul Fahmi, Hairuddin, Muhammad Asraf, Mohd Nordin, Atiqah Hamizah, Dahlan, Nofri Yenita, Mohd Yassin, Ihsan, Khirul Ashar, Nur Dalila

“…The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also takeninto account. Three models with different categories involving Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. …”
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Sentiment analysis and topic modelling of 2024 U.S. and Indonesian election tweets: a study of political discourse and public opinion by Widawati, Elisia Brispalma

“…This study investigates the effectiveness of various deep learning architectures and statistical models in both sentiment analysis and the temporal analysis of online public discourse through topic modelling and sentiment forecasting of tweets related to the 2024 Indonesian and U.S. elections. …”
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Landscape Water Storage and Subsurface Correlation from Satellite Surface Soil Moisture and Precipitation Observations by Short Gianotti, Daniel J., Salvucci, Guido D., Akbar, Ruzbeh, McColl, Kaighin A., Cuenca, Richard, Entekhabi, Dara

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