The Experts below are selected from a list of 261117 Experts worldwide ranked by ideXlab platform
Aytac Aydin - One of the best experts on this subject based on the ideXlab platform.
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an artificial neural network Model for predicting compression strength of heat treated woods and comparison with a multiple Linear Regression Model
2014Co-Authors: Sebahattin Tiryaki, Aytac AydinAbstract:Abstract This paper aims to design an artificial neural network Model to predict compression strength parallel to grain of heat treated woods, without doing comprehensive experiments. In this study, the artificial neural network results were also compared with multiple Linear Regression results. The results indicated that artificial neural network Model provided better prediction results compared to the multiple Linear Regression Model. Thanks to the results of this study, strength properties of heat treated woods can be determined in a short period of time with low error rates so that usability of such wood species for structural purposes can be better understood.
Wei Liu - One of the best experts on this subject based on the ideXlab platform.
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construction of exact simultaneous confidence bands for a simple Linear Regression Model
2008Co-Authors: Wei Liu, Shan Lin, Walter W PiegorschAbstract:A simultaneous confidence band provides a variety of inferences on the unknown components of a Regression Model. There are several recent papers using confidence bands for various inferential purposes; see for example, Sun et al. (1999), Spurrier (1999), Al-Saidy et al. (2003), Liu et al. (2004), Bhargava & Spurrier (2004), Piegorsch et al. (2005) and Liu et al. (2007). Construction of simultaneous confidence bands for a simple Linear Regression Model has a rich history, going back to the work of Working & Hotelling (1929). The purpose of this article is to consolidate the disparate modern literature on simultaneous confidence bands in Linear Regression, and to provide expressions for the construction of exact 1 ?? level simultaneous confidence bands for a simple Linear Regression Model of either one-sided or two-sided form. We center attention on the three most recognized shapes: hyperbolic, two-segment, and three-segment (which is also referred to as a trapezoidal shape and includes a constant-width band as a special case). Some of these expressions have already appeared in the statistics literature, and some are newly derived in this article. The derivations typically involve a standard bivariate t random vector and its polar coordinate transformation.
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Slope modified confidence bands for a simple Linear Regression Model
2006Co-Authors: Anthony J. Hayter, Henry P. Wynn, Wei LiuAbstract:Considerable attention has been directed in the statistical literature towards the construction of confidence bands for a simple Linear Regression Model. These confidence bands allow the experimenter to make inferences about the Model over a particular region of interest. However, in practice an experimenter will usually first check the significance of the Regression line before proceeding with any further inferences such as those provided by the confidence bands. From a theoretical point of view, this raises the question of what the conditional confidence level of the confidence bands might be, and from a practical point of view it is unsatisfactory if the confidence bands contain lines that are inconsistent with the directional decision on the slope. In this paper it is shown how confidence bands can be modified to alleviate these two problems
Chin-lu Chyu - One of the best experts on this subject based on the ideXlab platform.
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A fuzzy Linear Regression Model with better explanatory power
2002Co-Authors: Chiang Kao, Chin-lu ChyuAbstract:Previous studies on fuzzy Linear Regression analysis have a common characteristic of increasing spreads for the estimated fuzzy responses as the independent variable increases its magnitude, which is not suitable for general cases. This paper proposes a two-stage approach to construct the fuzzy Linear Regression Model. In the first stage, the fuzzy observations are defuzzified so that the traditional least-squares method can be applied to find a crisp Regression line showing the general trend of the data. In the second stage, the error term of the fuzzy Regression Model, which represents the fuzziness of the data in a general sense, is determined to give the Regression Model the best explanatory power for the data. The results from two examples, one with crisp data and the other with fuzzy data for the independent variable, indicate that the two-stage method proposed in this paper has better performance than the previous studies.
Sebahattin Tiryaki - One of the best experts on this subject based on the ideXlab platform.
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an artificial neural network Model for predicting compression strength of heat treated woods and comparison with a multiple Linear Regression Model
2014Co-Authors: Sebahattin Tiryaki, Aytac AydinAbstract:Abstract This paper aims to design an artificial neural network Model to predict compression strength parallel to grain of heat treated woods, without doing comprehensive experiments. In this study, the artificial neural network results were also compared with multiple Linear Regression results. The results indicated that artificial neural network Model provided better prediction results compared to the multiple Linear Regression Model. Thanks to the results of this study, strength properties of heat treated woods can be determined in a short period of time with low error rates so that usability of such wood species for structural purposes can be better understood.
Gauri Sankar Datta - One of the best experts on this subject based on the ideXlab platform.
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statistical disclosure control via sufficiency under the multiple Linear Regression Model
2018Co-Authors: Martin Klein, Gauri Sankar DattaAbstract:ABSTRACTIn this article we show, under the normal multiple Linear Regression Model, how synthetic data can be generated using the principle of sufficiency. An advantage of this approach is that if ...
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pseudo empirical bayes estimation of small area means under a nested error Linear Regression Model with functional measurement errors
2010Co-Authors: Gauri Sankar Datta, J N K Rao, Mahmoud TorabiAbstract:Abstract Small area estimation is studied under a nested error Linear Regression Model with area level covariate subject to measurement error. Ghosh and Sinha (2007) obtained a pseudo-Bayes (PB) predictor of a small area mean and a corresponding pseudo-empirical Bayes (PEB) predictor, using the sample means of the observed covariate values to estimate the true covariate values. In this paper, we first derive an efficient PB predictor by using all the available data to estimate true covariate values. We then obtain a corresponding PEB predictor and show that it is asymptotically “optimal”. In addition, we employ a jackknife method to estimate the mean squared prediction error (MSPE) of the PEB predictor. Finally, we report the results of a simulation study on the performance of our PEB predictor and associated jackknife MSPE estimator. Our results show that the proposed PEB predictor can lead to significant gain in efficiency over the previously proposed PEB predictor. Area level Models are also studied.