We study linear regressions in a context where the outcome of interest and some of the covariates are observed in two different datasets that cannot be matched. Traditional approaches obtain point ...

20 Superstars, two matches, one word... WarGames! The annual Survivor Series Premium Live Event returns on Saturday, November 29, when WWE takes over Petco Park and transforms the home of Major League ...

The Buffalo Bills haven't won in Houston since the J.P. Losman days, and that didn't change, dropping another one to the Texans, 23-19. The Texans' defense is No. 1 in the league for a reason, and ...

Abstract: Time-series calibration is essential for ensuring consistency and comparability across different Synthetic Aperture Radar (SAR) images, which is critical for long-term sequence analysis ...

Abstract: Regression models are employed in lossless compression of time series data, by storing the residual of each point, known as regression encoding. Owing to value fluctuation, the regression ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

What is Singular Spectrum Analysis (SSA)? Singular Spectrum Analysis (SSA) is a non-parametric technique in machine learning used to analyze and forecast time series data. SSA decomposes a time series ...

What if you could turn Excel into a powerhouse for advanced data analysis and automation in just a few clicks? Imagine effortlessly cleaning messy datasets, running complex calculations, or generating ...

Time-series data—measurements collected over time like stock prices or heart rates—plays a vital role in AI forecasting systems across industries. As these systems advance, the need for time-series ...

Introduction: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and ...