Computes trimmed mean of values, discarding values at the low and high end. The low and high values specify which part of the sample should be included in the mean, so e.g. low = 0.1 and high = 0.9 means 10% low and high values will be discarded....
To evaluate the estimation error of our model, 20% of the data for the samples unused in the training (9, 8 and 9 for Cu, Pb and As, respectively) were employed for the root mean square error (RMSE) and mean relative estimation error (MRE) calculation using Eqs. (1) and (2), gi...
Estimating the population mean It is often not enough to simply describe a set of data. Instead you want to make inferences about the parameters of the population the sample of data is drawn from. An inference may be an estimate of a parameter, or a hypothesis test if a parameter is ...
Energy dissipation rates are an important characteristic of turbulence; however, their magnitude in observational profiles can be incorrectly determined owing to their irregular appearance during vertical evolution. By analysing the data obtained from oceanic turbulence measurements, we demonstrate that the ve...
2.3 The information in zero defects. 22 2.4 Incorporating prior information. 25 2.4.1 Uniform prior. 26 2.4.2 Non-uniform prior. 27 Chapter 3: Capability Analysis Based on Rate of Nonconformities.31 3.1 Estimating the mean nonconformities per unit 31 ...
Mean particle diameters of histograms of size distributions were estimated by integration and by summation over the histogram intervals using several,partly model,size distributions.The summation method,used in the Moment-Ratio(M-R) method,tends to be more accurate than the Integration method and is...
The researchers found that the XGBoost model outperforms other numerical equations, achieving a high mean R2 of 0.93 and a low MAPE of 0.20 for testing. Their findings suggest that ML models offer a promising alternative to currently used mechanics-based models for design practice. Almustafa and ...
In addition, the generalized skew-Birnbaum–Saunders kernel density estimators are boundary bias free and achieve the optimal rate of convergence for the mean integrated squared error of the nonnegative asymmetric kernel estimators. We carry out a data analysis consisting of two parts. First, we ...
particularly concerning the detection of gradual changes in various dependent data sets. most of the earlier papers on the change analysis in autoregressive processes deal with abrupt changes, either in the mean or in the autoregressive parameters, respectively in the variance of the error process. ...
A panel is large if the number of cross-sectional units (or groups) and the number of time periods are going to infinity. It fits the following estimation methods: i) The Mean Group Estimator (MG, Pesaran and Smith 1995). ii) The Common Correlated Effects Estimator (CCE, Pesaran 2006),...