linear mean square error
Mean Square Error of Prediction in the Bornhuetter-Ferguson Claims Reserving Method
... conditional mean square error of prediction (MSEP) of the ultimate claim has been derived in the BF method until now, and as such, this paper aims to fill that ...generalized linear models ...
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Nonasymptotic bounds on the mean square error for MCMC estimates via renewal techniques
... where ν(dy)/P(x, dy) denote the Radon-Nikodym derivative (in practice, the ratio of densities). Mykland’s trick has been established in a number of practically relevant families (e.g. hierarchical linear models) ...
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A Comparative Study in Predicting Colon Rectum Cancer using Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) Models
... a linear combination of its own past values, past errors and current and past values by using the concept of time ...Non Linear Technique used for prediction that involve learning and pattern ...the ...
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Performance Analysis of LMS and Normalized LMS Adaptive Filter Algorithms
... plot mean of LMS filter is moving to the left(negative mean) and Mean moves to the right(Positive Mean) in Normalized ...the mean value of the filter. Normalized LMS filter shows ...
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Stability analysis for yield and quality traits in wheat (Triticum aestivum L.)¬¬¬¬
... pooled error. The regression analysis showed that the mean sum of square due to environment (Linear) was highly significant for all the trait when tested against pooled error, hence ...
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De-Noising Of Speech Signal Using Adaptive Filter Algorithms
... Least Mean Square (LMS) filter is a linear adaptive ...estimated error by comparing the output signal and desired signal b) adaptation process – according to the estimate error, it ...
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Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
... 3.11 Linear vector quantization network architecture 70 4.1 Analysis of mean square error for 21 mother wavelets function 75 ...4.3 Mean square error performance of ...
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Improvement of the Preliminary Test Estimator When Stochastic Restrictions are Available in Linear Regression Model
... multiple linear regression model when multi- colinearity exists among predictor ...of mean square error matrix (MSEM) and scalar mean square error (SMSE) for the two cases ...
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Detecting and Preventing DDoS Attacks in Cloud
... the mean value of distance in the next time period by using the exponential smoothing estimation ...(Minimum Mean Square Error) linear predictor to estimate the traffic rates from ...
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Performance of LTE Channel Estimation Algorithms for Different Interpolation Methods and Modulation Schemes
... and Linear Minimum Mean Square Error (LMMSE) for different interpolation methods and modulation ...the mean squared error (MSE) as the performance metric of interest for downlink ...
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An analysis of nonlinear behavior in delta-sigma modulators
... A new method of analysis for Delta-Sigma Modulators based on modeling the non- linear quantizer with minimum-mean-square error linearized gains followed by an additive noise source repre[r] ...
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MIMO Assisted Space Code Division Multiple Access: Linear Detectors and Performance over Multipath Fading Channels
... of linear single-user and multiuser detectors (MUDs), namely, correlation, decorrelating, and minimum mean-square error (MMSE) detectors [21], for the synchronous MIMO SCDMA systems is ...
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A technique of fuzzy c mean in multiple linear regression model toward paddy yield
... multiple linear regression model and fuzzy c-means ...using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear ...
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Performance and Analysis of Channel Estimation Techniques for LTE Downlink System under Fading with Mobility
... two linear channel estimators for LTE Downlink systems, the Least Square Error (LSE) and the Linear Minimum Mean Square Error (LMMSE) using QPSK and 16 QAM modulation ...
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Sentiment Analysis of Social Media Text Data using Back Propagation in Artificial Neural Networks
... It can be concluded from the mathematical background and subsequent results that the proposed system yields high accuracy for text mining data. The data used here are in the form of tweets. The training algorithm used is ...
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Analysing the temporal dynamics of model performance for hydrological models
... time error (error 4) and the most ex- treme overestimating errors due to peak size with correct in- tegral and undetected peaks are found in this ...extreme error shift- ing the modelled below the ...
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Forecasting Industrial Production in Iran: A Comparative Study of Artificial Neural Networks and Adaptive Nero-Fuzzy Inference System
... Forecasting industrial production is essential for efficient planning by managers. Although there are many statistical and mathematical methods for prediction, the use of intelligent algorithms with desirable features ...
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The Implementation of Forecasting Method by Incorporating Human Judgment
... forecasting error method that is used in this research is Mean Absolute Error (MAE), Mean Square Error (MSE), and Standard Deviation error ...forecasting error ...
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Mean Square Error of Blind Channel Estimation Using a Constant Modulus Algorithm
... consists of bits stream, modulated symbol, OFDM signal and RF transmit antenna. The bit stream consists of general data, Forward Error Correction (FEC) and bit inter leaver. This serves as input to the QAM ...
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Call option price function in Bernstein polynomial basis with no arbitrage inequality constraints
... In this study, we use a Bernstein polynomial basis [] to estimate the arbitrage-free op- tion price function. The choice of this basis is straightforward as the constraints of mono- tonicity, convexity, and other ...
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