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Journal articles on the topic 'Error variance'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

GLASBEY, C. A. "Standard errors resilient to error variance misspecification." Biometrika 75, no. 2 (1988): 201–6. http://dx.doi.org/10.1093/biomet/75.2.201.

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Bishop, Craig H., and Elizabeth A. Satterfield. "Hidden Error Variance Theory. Part I: Exposition and Analytic Model." Monthly Weather Review 141, no. 5 (2013): 1454–68. http://dx.doi.org/10.1175/mwr-d-12-00118.1.

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Abstract A conundrum of predictability research is that while the prediction of flow-dependent error distributions is one of its main foci, chaos fundamentally hides flow-dependent forecast error distributions from empirical observation. Empirical estimation of such error distributions requires a large sample of error realizations given the same flow-dependent conditions. However, chaotic elements of the flow and the observing network make it impossible to collect a large enough conditioned error sample to empirically define such distributions and their variance. Such conditional variances are

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Satterfield, Elizabeth A., and Craig H. Bishop. "Heteroscedastic Ensemble Postprocessing." Monthly Weather Review 142, no. 9 (2014): 3484–502. http://dx.doi.org/10.1175/mwr-d-13-00286.1.

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Ensemble variances provide a prediction of the flow-dependent error variance of the ensemble mean or, possibly, a high-resolution forecast. However, small ensemble size, unaccounted for model error, and imperfections in ensemble generation schemes cause the predictions of error variance to be imperfect. In previous work, the authors developed an analytic approximation to the posterior distribution of true error variances, given an imperfect ensemble prediction, based on parameters recovered from long archives of innovation and ensemble variance pairs. This paper shows how heteroscedastic postp

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Ni, Chengcai, and Lianjun Zhang. "An Estimator of Prediction Error Variance for Projection Equations." Forest Science 54, no. 6 (2008): 569–78. http://dx.doi.org/10.1093/forestscience/54.6.569.

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Abstract An estimator of prediction error variance for projection equations was derived using the first-order Taylor expansion in this study. The estimator, a modified estimator of the prediction error variance for a population mean regression model, was adapted for situations in which projection equations are applied to unsampled individuals. The estimator accounted for the errors associated with the response variable on the right side of a projection equation, as well as the errors associated with parameter estimation and serial correlations in data. The application of the estimator was demo

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Bishop, Craig H., Elizabeth A. Satterfield, and Kevin T. Shanley. "Hidden Error Variance Theory. Part II: An Instrument That Reveals Hidden Error Variance Distributions from Ensemble Forecasts and Observations." Monthly Weather Review 141, no. 5 (2013): 1469–83. http://dx.doi.org/10.1175/mwr-d-12-00119.1.

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Abstract In Part I of this study, a model of the distribution of true error variances given an ensemble variance is shown to be defined by six parameters that also determine the optimal weights for the static and flow-dependent parts of hybrid error variance models. Two of the six parameters (the climatological mean of forecast error variance and the climatological minimum of ensemble variance) are straightforward to estimate. The other four parameters are (i) the variance of the climatological distribution of the true conditional error variances, (ii) the climatological minimum of the true co

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Lappi, Juha. "Estimating the distribution of a variable measured with error: stand densities in a forest inventory." Canadian Journal of Forest Research 21, no. 4 (1991): 469–73. http://dx.doi.org/10.1139/x91-063.

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If a variable is measured (or estimated) with error, then the distribution of the measurements is flatter than the true distribution. The variance of a measured variable is the sum of the true variance and the measurement error variance. If we shrink measured values towards their mean so that the variance will be equal to the true population variance, or its estimate, the obtained empirical distribution is more similar to the true distribution than is the distribution of measured values. To estimate the population variance, an estimate of the variance of measurement errors is required. If stan

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Ukpong, Isonguyo Michae, and Emmanuel Wilfred Okereke. "Inverse cube root transformation: Theory and application to time series data." Communication in Physical Sciences 12, no. 2 (2025): 741–69. https://doi.org/10.4314/cps.v12i2.7.

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Constant variance assumption is one of the necessary assumptions of several time series models. The violation of this assumption often necessitates data transformation. This paper investigated the properties of the inverse cube root transformation of a time series based on the multiplicative model. The probability density function of the inverse cube root of the left truncated normally distributed error components of the model was derived; the relationship between the variances of the errors in both the transformed and untransformed multiplicative models was examined through simulations. The e

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Takezawa, Kunio. "Error Variance for AIC." Japanese Journal of Applied Statistics 40, no. 2 (2011): 81–86. http://dx.doi.org/10.5023/jappstat.40.81.

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Fekri, Majid, and M. K. Yau. "A Study of Rain Forecast Error Structure Based on Radar Observations over a Continental-Scale Spatial Domain." Monthly Weather Review 144, no. 8 (2016): 2871–87. http://dx.doi.org/10.1175/mwr-d-15-0191.1.

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Abstract This study examines the univariate error covariances of hourly rainfall accumulations using two different NWP models and a mosaic of radar reflectivity over a continental-scale domain. The study focuses on two main areas. The focus of the first part of the paper is on the ensemble-based and the innovation-based error variance and correlation estimations. An ensemble of forecasts and a set of observations provide the basis for estimating the errors in two different ways. The results indicate that both ensemble- and innovation-based methods lead to comparable variance estimations, while

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10

Anthes, Richard, and Therese Rieckh. "Estimating observation and model error variances using multiple data sets." Atmospheric Measurement Techniques 11, no. 7 (2018): 4239–60. http://dx.doi.org/10.5194/amt-11-4239-2018.

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Abstract. In this paper we show how multiple data sets, including observations and models, can be combined using the “three-cornered hat” (3CH) method to estimate vertical profiles of the errors of each system. Using data from 2007, we estimate the error variances of radio occultation (RO), radiosondes, ERA-Interim, and Global Forecast System (GFS) model data sets at four radiosonde locations in the tropics and subtropics. A key assumption is the neglect of error covariances among the different data sets, and we examine the consequences of this assumption on the resulting error estimates. Our

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Karspeck, Alicia R. "An Ensemble Approach for the Estimation of Observational Error Illustrated for a Nominal 1° Global Ocean Model." Monthly Weather Review 144, no. 5 (2016): 1713–28. http://dx.doi.org/10.1175/mwr-d-14-00336.1.

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Least squares algorithms for data assimilation require estimates of both background error covariances and observational error covariances. The specification of these errors is an essential part of designing an assimilation system; the relative sizes of these uncertainties determine the extent to which the state variables are drawn toward the observational information. Observational error covariances are typically computed as the sum of measurement/instrumental errors and “representativeness error.” In a coarse-resolution ocean general circulation model the errors of representation are the domi

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Zhang, Wei, Bainian Liu, Weimin Zhang, Shaoying Li, Xiaoqun Cao, and Xiang Xing. "Effect of Flow-Dependent Unbalanced Background Error Variances on Tropical Cyclone Forecasting." Journal of Marine Science and Engineering 10, no. 11 (2022): 1653. http://dx.doi.org/10.3390/jmse10111653.

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The background error variance in variational data assimilation can significantly affect a model’s initial field. Around extreme weather events, the variance of the unbalanced control variables have contributed highly to the total variance. This study investigates the effect of flow-dependent unbalanced variance on tropical cyclone (TC) forecasts using the ensemble of data assimilation (EDA) method. The analysis of TC Saudel (October 2020) shows that flow-dependent unbalanced variances can better represent the uncertainty in the background error, which is investigated in terms of magnitude and

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Ninness, Brett, and Håkan Hjalmarsson. "ACCURATE QUANTIFICATION OF VARIANCE ERROR." IFAC Proceedings Volumes 35, no. 1 (2002): 271–76. http://dx.doi.org/10.3182/20020721-6-es-1901.00456.

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Ninness, Brett, and Håkan Hjalmarsson. "EXACT QUANTIFICATION OF VARIANCE ERROR." IFAC Proceedings Volumes 35, no. 1 (2002): 277–82. http://dx.doi.org/10.3182/20020721-6-es-1901.00457.

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Alıca, Selin, Şenay Açıkgöz, Rukiye Dağalp, and Şahika Gökmen. "Comparison of performances of heteroskedasticity tests under measurement error." Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 74, no. 2 (2025): 333–45. https://doi.org/10.31801/cfsuasmas.1632865.

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While measurement error has an impact on the unbiasedness of the ordinary least squares (OLS) estimator, the heteroskedastic error term causes inefficient OLS estimators and biased variance estimates. Although the econometric literature has answers to these two fundamental concerns, such as applying measurement error correction methods and heteroskedasticity-robust standard errors, they do not directly address testing heteroskedasticity. This paper investigates the power of the most commonly used heteroskedasticity tests in the presence of error-in-variables. Monte Carlo simulations under diff

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Korendijk, Elly J. H., Cora J. M. Maas, Mirjam Moerbeek, and Peter G. M. Van der Heijden. "The Influence of Misspecification of the Heteroscedasticity on Multilevel Regression Parameter and Standard Error Estimates." Methodology 4, no. 2 (2008): 67–72. http://dx.doi.org/10.1027/1614-2241.4.2.67.

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Like in ordinary regression models, in multilevel analysis, homoscedasticity of the residual variances is an assumption that is mostly unchecked. However, in experimental research, the residual variance component at level two may differ in the experimental and the control condition, leading to heteroscedastic second level variances. Using a simulation study, the consequences of ignoring second level heteroscedasticity on the estimation of the fixed and random parameters and their standard errors was investigated. It was found that the standard error of the second level variance is underestimat

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17

Wan, Fei. "Analyzing pre-post designs using the analysis of covariance models with and without the interaction term in a heterogeneous study population." Statistical Methods in Medical Research 29, no. 1 (2019): 189–204. http://dx.doi.org/10.1177/0962280219827971.

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Pre-post parallel group randomized designs have been frequently used to compare the effectiveness of competing treatment strategies and the ordinary least squares (OLS)-based analysis of covariance model (ANCOVA) is a routine analytic approach. In many scenarios, the associations between the baseline and the post-randomization scores could differ between the treatment and control arms, which justifies the inclusion of the treatment by baseline score interaction in ANCOVA. This heterogeneity may also cause heteroscedastic errors in ANCOVA. In this study, we compared the performances of the ANCO

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18

Zhang, Yun Feng. "Prediction Error Identification for Closed Loop System with Errors-in-Variables." ECS Transactions 107, no. 1 (2022): 353–65. http://dx.doi.org/10.1149/10701.0353ecst.

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In order to study whether the predictive error method can also obtain satisfactory results in the closed-loop system with variable errors, the closed-loop situation is deduced by imitating the predictive error method in the open-loop system. The results show that the prediction error method can be used to identify the classical closed-loop system. When the input and output noise are all white noise with mean 0 and variance 1, the closed-loop system with errors-in-variables can be used to identify the prediction error.

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19

Delaigle, Aurore, and Peter Hall. "Estimation of observation-error variance in errors-in-variables regression." Statistica Sinica 21, no. 3 (2011): 1023–63. http://dx.doi.org/10.5705/ss.2009.039.

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20

Oberski, Daniel L., and Albert Satorra. "Measurement Error Models With Uncertainty About the Error Variance." Structural Equation Modeling: A Multidisciplinary Journal 20, no. 3 (2013): 409–28. http://dx.doi.org/10.1080/10705511.2013.797820.

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21

Hodyss, Daniel, William F. Campbell, and Jeffrey S. Whitaker. "Observation-Dependent Posterior Inflation for the Ensemble Kalman Filter." Monthly Weather Review 144, no. 7 (2016): 2667–84. http://dx.doi.org/10.1175/mwr-d-15-0329.1.

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Abstract Ensemble-based Kalman filter (EBKF) algorithms are known to produce posterior ensembles whose variance is incorrect for a variety of reasons (e.g., nonlinearity and sampling error). It is shown here that the presence of sampling error implies that the true posterior error variance is a function of the latest observation, as opposed to the standard EBKF, whose posterior variance is independent of observations. In addition, it is shown that the traditional ensemble validation tool known as the “binned spread-skill” diagram does not correctly identify this issue in the ensemble generatio

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22

Yilmaz, M. Tugrul, and Wade T. Crow. "Evaluation of Assumptions in Soil Moisture Triple Collocation Analysis." Journal of Hydrometeorology 15, no. 3 (2014): 1293–302. http://dx.doi.org/10.1175/jhm-d-13-0158.1.

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Abstract Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and orthogonality with respect to the truth) are required for TCA estimates to be unbiased. Even though soil moisture studies commonly acknowledge that these assumptions are required for an unbiased TCA, no study has specifically investigated the degree to which errors in existing soil moisture da

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23

Dozie, K. C. N., and M. U. Uwaezuoke. "The Proposed Buys-Ballot Estimates for Multiplicative Model with the Error Variances." Journal of Engineering Research and Reports 25, no. 8 (2023): 94–106. http://dx.doi.org/10.9734/jerr/2023/v25i8962.

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This article presents the condition(s) under which the multiplicative model with the error variances best describes the pattern in an observed time series, while comparing it with those of the additive and mixed models. The method of estimation is based on the periodic, seasonal and overall averages and variances of time series data arranged in a Buys-Ballot table. The method assumes that (1) the underlying distribution of the variable, X i j , i = 1, 2, ..., m , j = 1 , 2 , ..., s , under study is normal. (2) the trending curve is linear (3) the decomposition method is either additive or mult

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Satterfield, Elizabeth A., Daniel Hodyss, David D. Kuhl, and Craig H. Bishop. "Observation-Informed Generalized Hybrid Error Covariance Models." Monthly Weather Review 146, no. 11 (2018): 3605–22. http://dx.doi.org/10.1175/mwr-d-18-0016.1.

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Abstract Because of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble-derived covariance matrix is equal to the true error covariance matrix. Here, we describe a simple and intuitively compelling method to fit calibration functions of the ensemble sample variance to the mean of the distribution of true error variances, given an ensemble estimate. We demonstrate that the use of such calibration functions is consistent with theory showing that, when sampling error in the prior variance estimate is considered, the gain that minimizes the posterior error vari

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沙达克提, ·艾力. "Estimation of Variable Points of Variance Model with Measurement Error." Advances in Applied Mathematics 13, no. 02 (2024): 877–90. http://dx.doi.org/10.12677/aam.2024.132083.

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26

Zhan, Jie, Lin Yuan, and Yin Lin Wu. "Accuracy Analysis of Highly Kinematical Positioning of GPS." Applied Mechanics and Materials 602-605 (August 2014): 1847–50. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1847.

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For the accuracy of highly kinematical positioning of GPS carrier phase is not easy to be obtained directly, a data processing method is presented based on the analysis of several common kinematical positioning accuracy analytical methods. The method firstly analyzes and models the state of motion carrier, and then conducts a statistics variable composed by multiple positioning data , Finally uses a determined relation between the variance of the statistics variable and the variance of the positioning error to gain the variance of positioning error by calculating the variance of the statistics

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Hendrickx, Marit G. A., Jan Vanderborght, Pieter Janssens, et al. "Pooled error variance and covariance estimation of sparse in situ soil moisture sensor measurements in agricultural fields in Flanders." SOIL 11, no. 1 (2025): 435–56. https://doi.org/10.5194/soil-11-435-2025.

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Abstract. Accurately quantifying errors in soil moisture measurements from in situ sensors at fixed locations is essential for reliable state and parameter estimation in probabilistic soil hydrological modeling. This quantification becomes particularly challenging when the number of sensors per field or measurement zone (MZ) is limited. When direct calculation of errors from sensor data in a certain MZ is not feasible, we propose to pool systematic and random errors of soil moisture measurements for a specific measurement setup and derive a pooled error covariance matrix that applies to this s

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Cacciafesta, Fabrizio. "Visualizing the Variance of a Random Variable." Open Systems & Information Dynamics 18, no. 01 (2011): 71–85. http://dx.doi.org/10.1142/s1230161211000054.

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We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

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Ying, Yue. "Assimilating Observations with Spatially Correlated Errors Using a Serial Ensemble Filter with a Multiscale Approach." Monthly Weather Review 148, no. 8 (2020): 3397–412. http://dx.doi.org/10.1175/mwr-d-19-0387.1.

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Abstract The serial ensemble square root filter (EnSRF) typically assumes observation errors to be uncorrelated when assimilating the observations one at a time. This assumption causes the filter solution to be suboptimal when the observation errors are spatially correlated. Using the Lorenz-96 model, this study evaluates the suboptimality due to mischaracterization of observation error spatial correlations. Neglecting spatial correlations in observation errors results in mismatches between the specified and true observation error variances in spectral space, which cannot be resolved by inflat

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Wang, Zhanze, Feifeng Liu, Simin He, and Zhixiang Xu. "A Spatial Variant Motion Compensation Algorithm for High-Monofrequency Motion Error in Mini-UAV-Based BiSAR Systems." Remote Sensing 13, no. 17 (2021): 3544. http://dx.doi.org/10.3390/rs13173544.

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High-frequency motion errors can drastically decrease the image quality in mini-unmanned-aerial-vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR), where the spatial variance is much more complex than that in monoSAR. High-monofrequency motion error is a special BiSAR case in which the different motion errors from transmitters and receivers lead to the formation of monofrequency motion error. Furthermore, neither of the classic processors, BiSAR and monoSAR, can compensate for the coupled high-monofrequency motion errors. In this paper, a spatial variant motion compensation algorith

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Kukush, Alexander, and Igor Mandel. "A validity test for a multivariate linear measurement error model." Model Assisted Statistics and Applications 19, no. 1 (2024): 97–115. http://dx.doi.org/10.3233/mas-231494.

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A criterion is proposed for testing the hypothesis about the nature of the error variance in the dependent variable in a linear model, which separates correctly and incorrectly specified models. In the former one, only the measurement errors determine the variance (i.e., the dependent variable is correctly explained by the independent ones, up to measurement errors), while the latter model lacks some independent covariates (or has a nonlinear structure). The proposed MEMV (Measurement Error Model Validity) test checks the validity of the model when both dependent and independent covariates are

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32

West, B. T., and K. Olson. "How Much of Interviewer Variance is Really Nonresponse Error Variance?" Public Opinion Quarterly 74, no. 5 (2010): 1004–26. http://dx.doi.org/10.1093/poq/nfq061.

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Luiz, T. S. J., V. C. G. Souza, and J. C. Koppe. "Use of extension variance in monitoring of fluoride in bottled water." Water Supply 20, no. 8 (2020): 3281–87. http://dx.doi.org/10.2166/ws.2020.232.

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Abstract Temporal variograms allowed the analyzing of the temporal variance of eight sources of mineral waters during the four climatic seasons. The water sources are located in the state of São Paulo, Brazil. The extension variance compares the temporal variance obtained in the collection interval t with the temporal variance obtained in the collection interval T (where T is twice as large as t). Based on the calculation of the extension variance, relative sampling errors for the confidence intervals (CI) equal to 68% and 99% were obtained. For the sampled sources, the greater the sampling in

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Resende, Tulio Viegas Bicalho, and José Aurélio Medeiros da Luz. "Error variance of short duration sieving." REM - International Engineering Journal 71, no. 2 (2018): 305–11. http://dx.doi.org/10.1590/0370-44672015710008.

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Khrabrov, A. V., and B. U. Ö. Sonnerup. "Error estimates for minimum variance analysis." Journal of Geophysical Research: Space Physics 103, A4 (1998): 6641–51. http://dx.doi.org/10.1029/97ja03731.

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36

Mahata, Kaushik. "Variance error, interpolation and experiment design." Automatica 49, no. 5 (2013): 1117–25. http://dx.doi.org/10.1016/j.automatica.2013.01.021.

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Horrace, William C., and Christopher F. Parmeter. "Semiparametric deconvolution with unknown error variance." Journal of Productivity Analysis 35, no. 2 (2010): 129–41. http://dx.doi.org/10.1007/s11123-010-0193-z.

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Spiegelman, Donna, Roger Logan, and Douglas Grove. "Regression Calibration with Heteroscedastic Error Variance." International Journal of Biostatistics 7, no. 1 (2011): 1–34. http://dx.doi.org/10.2202/1557-4679.1259.

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SHIMIZU, Kazuaki. "Communality, specificity, error variance, and uniqueness." Proceedings of the Annual Convention of the Japanese Psychological Association 75 (September 15, 2011): 1AM037. http://dx.doi.org/10.4992/pacjpa.75.0_1am037.

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Wang, Ching-Wei, and Andrew Hunter. "A low variance error boosting algorithm." Applied Intelligence 33, no. 3 (2009): 357–69. http://dx.doi.org/10.1007/s10489-009-0172-0.

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Walsh, David M., Kathleen D. Walsh, and John P. Evans. "Assessing estimation error in a tracking error variance minimisation framework." Pacific-Basin Finance Journal 6, no. 1-2 (1998): 175–92. http://dx.doi.org/10.1016/s0927-538x(98)00005-5.

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Fischer, Micha, Brady T. West, Michael R. Elliott, and Frauke Kreuter. "The Impact of Interviewer Effects on Regression Coefficients." Journal of Survey Statistics and Methodology 7, no. 2 (2018): 250–74. http://dx.doi.org/10.1093/jssam/smy007.

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Abstract This article examines the influence of interviewers on the estimation of regression coefficients from survey data. First, we present theoretical considerations with a focus on measurement errors and nonresponse errors due to interviewers. Then, we show via simulation which of several nonresponse and measurement error scenarios has the biggest impact on the estimate of a slope parameter from a simple linear regression model. When response propensity depends on the dependent variable in a linear regression model, bias in the estimated slope parameter is introduced. We find no evidence t

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Palla, Ilir. "The Comparison of Some Methods in Analysis of Linear Regression Using R Software." European Journal of Engineering and Formal Sciences 3, no. 3 (2019): 22. http://dx.doi.org/10.26417/ejef.v3i3.p22-31.

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This article contains the OLS method, WLS method and bootstrap methods to estimate coefficients of linear regression and their standard deviation. If regression holds random errors with constant variance and if those errors are independent normally distributed we can use least squares method, which is accurate for drawing inferences with these assumptions. If the errors are heteroscedastic, meaning that their variance depends from explanatory variable, or have different weights, we can’t use least squares method because this method cannot be safe for accurate results. If we know weights for ea

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Vincent, Odhiambo, Hellen Waititu, and Nyakundi Omwando Cornelious. "Nonparametric Estimation of Error Variance under Simple Random Sampling without Replacement." International Journal of Mathematics And Computer Research 10, no. 10 (2022): 2925–33. http://dx.doi.org/10.47191/ijmcr/v10i10.02.

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This study adopts a nonparametric approach in the estimation of a finite population error variance in the setting where the variance is a constant (homoscedastic) using a model-based technique under simple random sampling without replacement (SRSWOR). A mean square analysis of the estimator has been conducted, including the asymptotic behaviour of the estimator and the results show that the asymptotic distribution in a homoscedastic setting is asymptotically unbiased and consistent. The performance of the developed estimator is compared to that of other existing estimators using real data. R s

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Blanc, Sebastian M., and Thomas Setzer. "Bias–Variance Trade-Off and Shrinkage of Weights in Forecast Combination." Management Science 66, no. 12 (2020): 5720–37. http://dx.doi.org/10.1287/mnsc.2019.3476.

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Combining forecasts is an established approach for improving forecast accuracy. So-called optimal weights (OWs) estimate combination weights by minimizing errors on past forecasts. Yet the most successful and common approach ignores all training data and assigns equal weights (EWs) to forecasts. We analyze this phenomenon by relating forecast combination to statistical learning theory, which decomposes forecast errors into three components: bias, variance, and irreducible error. In this framework, EWs minimize the variance component (errors resulting from estimation uncertainty) but ignore the

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Ranke, Johannes, and Stefan Meinecke. "Error Models for the Kinetic Evaluation of Chemical Degradation Data." Environments 6, no. 12 (2019): 124. http://dx.doi.org/10.3390/environments6120124.

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In the kinetic evaluation of chemical degradation data, degradation models are fitted to the data by varying degradation model parameters to obtain the best possible fit. Today, constant variance of the deviations of the observed data from the model is frequently assumed (error model “constant variance”). Allowing for a different variance for each observed variable (“variance by variable”) has been shown to be a useful refinement. On the other hand, experience gained in analytical chemistry shows that the absolute magnitude of the analytical error often increases with the magnitude of the obse

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Ribeiro Neto, Homero, Marciel Lelis Duarte, and Nerilson Terra Santos. "Evaluation of empirical type I error rates of F and normality tests under different variance and mean conditions in multi-treatment CRDs." Multi-Science Journal 8, no. 1 (2025): 1–9. https://doi.org/10.33837/msj.v8i1.1719.

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Hypothesis tests, such as normality tests, are extensively employed in Agricultural Sciences to evaluate the normality assumption of the F test in the Analysis of Variance (ANOVA) when large sample sizes are unavailable. Nonetheless, researchers conducting these tests are exposed to the risk of committing type I or type II errors, with probabilities that are influenced by different experimental conditions. This study assesses the empirical type I error rate of hypothesis tests by considering the equality (inequality) of treatment means, the homogeneity (heterogeneity) of variances, and differe

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48

Wheway, Virginia. "Variance reduction trends on ‘boosted’ classifiers." Journal of Applied Mathematics and Decision Sciences 8, no. 3 (2004): 141–54. http://dx.doi.org/10.1155/s1173912604000094.

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Ensemble classification techniques such as bagging, (Breiman, 1996a), boosting (Freund & Schapire, 1997) and arcing algorithms (Breiman, 1997) have received much attention in recent literature. Such techniques have been shown to lead to reduced classification error on unseen cases. Even when the ensemble is trained well beyond zero training set error, the ensemble continues to exhibit improved classification error on unseen cases. Despite many studies and conjectures, the reasons behind this improved performance and understanding of the underlying probabilistic structures remain open and c

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49

Pospelov, Boris, Evgenіy Rybka, Mikhail Samoilov, et al. "Investigating errors when forecasting processes with uncertain dynamics and observation noise by the self-adjusting brown's zero-order model." Eastern-European Journal of Enterprise Technologies 6, no. 9 (114) (2021): 47–53. http://dx.doi.org/10.15587/1729-4061.2021.244623.

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This paper reports a study into the errors of process forecasting under the conditions of uncertainty in the dynamics and observation noise using a self-adjusting Brown's zero-order model. The dynamics test models have been built for predicted processes and observation noises, which make it possible to investigate forecasting errors for the self-adjusting and adaptive models. The test process dynamics were determined in the form of a rectangular video pulse with a fixed unit amplitude, a radio pulse of the harmonic process with an amplitude attenuated exponentially, as well as a video pulse wi

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50

Boris, Pospelov, Rybka Evgenіy, Samoilov Mikhail, et al. "Investigating errors when forecasting processes with uncertain dynamics and observation noise by the self-adjusting brown's zero-order model." Eastern-European Journal of Enterprise Technologies 6, no. 9 (114) (2021): 47–53. https://doi.org/10.15587/1729-4061.2021.244623.

Full text

Abstract:

This paper reports a study into the errors of process forecasting under the conditions of uncertainty in the dynamics and observation noise using a self-adjusting Brown's zero-order model. The dynamics test models have been built for predicted processes and observation noises, which make it possible to investigate forecasting errors for the self-adjusting and adaptive models. The test process dynamics were determined in the form of a rectangular video pulse with a fixed unit amplitude, a radio pulse of the harmonic process with an amplitude attenuated exponentially, as well as a video puls

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