Diebold Mariano Test
Diebold Mariano Test - Related Videos
07:58
Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
0 Views •
Cited by 10 •
2020
Here, we present a protocol to use a curvelet transform-based, open-source MATLAB software tool for quantifying fibrillar collagen organization in the extracellular matrix of both normal and diseased tissues. This tool can be applied to images with collagen fibers or other types of line-like structures.
09:30
Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies
0 Views •
Cited by 2 •
2023
The protocol summarizes the best practices to minimize microbial bioburden in a cleanroom environment and includes strategies such as environmental monitoring, process monitoring, and product sterility testing. It is relevant for manufacturing and testing facilities that are required to meet current good tissue practice standards and current good manufacturing practice standards.
09:31
X-ray Diffraction
0 Views •
2023
Source: Faisal Alamgir, School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA X-ray diffraction (XRD) is a technique used in materials science for determining the atomic and molecular structure of a material. This is done by irradiating a sample of the material with incident X-rays and then measuring the intensities and scattering angles of the X-rays that are scattered by the material. The intensity of the scattered X-rays are plotted as a function of the...
11:34
Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
0 Views •
Cited by 7 •
2017
This method aims at locating vertical subsurface defects. Here, we couple a laser with a spatial light modulator and trigger its video input to heat a sample surface deterministically with two anti-phased modulated lines while acquiring highly resolved thermal images. The defect position is retrieved from evaluating thermal wave interference minima.
09:40
Measurement and Analysis of Atomic Hydrogen and Diatomic Molecular AlO, C2, CN, and TiO Spectra Following Laser-induced Optical Breakdown
0 Views •
Cited by 8 •
2014
Time-resolved atomic and diatomic molecular species are measured using LIBS. The spectra are collected at various time delays following the generation of optical breakdown plasma with Nd:YAG laser radiation and are analyzed to infer electron density and temperature.
FAQs
The loss differential is formed by comparing the errors from two forecasts under the same chosen loss function at each time point. Its average represents the observed difference in predictive performance. The test then evaluates whether that mean differs significantly from zero, distinguishing a consistent advantage from an apparent difference caused by variation across the forecast period.
Forecast errors and their loss differentials can be related across successive time points rather than behaving as independent observations. Serial correlation can therefore affect the estimated variability of the mean differential. A robust variance estimate accounts for this dependence, making the statistical comparison more appropriate for time-series forecasts used in engineering systems.
The loss function determines how forecast errors are translated into performance penalties before the two models are compared. Consequently, the result reflects the engineering meaning assigned to error, rather than an abstract accuracy measure alone. Selecting a suitable loss function helps align the test with whether demand, failure, energy, or signal prediction errors matter most.
A mean differential near zero indicates little average separation between the competing forecasts under the selected loss function. The test examines whether that observed difference is statistically distinguishable from zero, rather than treating any numerical advantage as decisive. This helps engineers avoid selecting a model solely because it performs slightly better in a particular sample.
An engineer first identifies two competing forecasts and the corresponding observed time-series outcomes. Errors are evaluated with a common loss function, producing a sequence of loss differentials. The mean differential is then tested against zero using a variance estimate that accommodates possible serial correlation. The resulting comparison supports a reasoned choice between predictive models.
The method can compare forecasts for engineering quantities such as demand, failures, energy use, and signals. In each case, it indicates whether one model’s apparent predictive advantage is statistically meaningful under the selected error measure. This evidence can support model selection and contribute to more dependable predictive systems where forecast quality influences engineering decisions.