Mean Square Error Matrix
Improvement of the Preliminary Test Estimator When Stochastic Restrictions are Available in Linear Regression Model
... of mean square error matrix (MSEM) and scalar mean square error (SMSE) for the two cases in which the stochastic restrictions are correct and not ...
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Efficient Implementation of Complex Matrix Inversion for LMMSE Decoder
... minimum mean square error (LMMSE) due to its low ...complex matrix inversion method is Alamouti blockwise analytic matrix inversion (ABAMI), which achieves good trade-off between ...
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Orthogonal least squares regression with tunable kernels
... covariance matrix of individual regressor by incrementally minimising the training mean square error (MSE) in an orthogonal forward selection procedure using a guided random search algorithm, ...
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Orthogonal Least Square with Boosting for Regression
... least square method with boosting. This technique tunes the mean vector and diagonal covariance matrix of individual re- gressor by incrementally minimizing the training mean square ...
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ICI cancellation using normalized minimum mean square error in MIMO OFDM systems
... (OFDMA) is a channel estimation technique proposed. Here the effects of coarse timing error and multipath propagation were considered jointly. An optimistic scenario alone considered in many conventional ...
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A De Noising Method for Track State Detection Signal Based on EMD
... attitude matrix transformation, transformation to the geographic coordinates, then the EMD decomposition based on continuous root mean square error ...
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Multiuser Detection Using Adaptive Multistage Matrix Wiener Filtering Schemes with Stage-Selection Criteria in DS-UWB
... minimum mean- square error (MMSE) receiver [12] is capable to suppress the MAI ...the error propagation occurs ...autocorrelation matrix, which involves an expensive computational ...
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Statistical Study of Least Mean Square and Normalised Least Mean Square Algorithms
... A significant aspect of adaptive filter functioning is the ability to track the time fluctuations of the fundamental signal statistics. The standard analytical model accepts the input signal is stationary. However, a ...
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Mean square error optimal weighting for multitaper cepstrum estimation
... In all periodogram-based spectrum analysis methods, the multitaper estimation method can be considered to be a filtering procedure in a FIR-filter bank where the filter functions all can be modulated to be an identical ...
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A robust and forward-Looking industrial production indicator
... 5 not find them better than the much simpler univariate model. We tried the Vector Error- Correction (VEC) Model, in addition to the Vector Auto-Regressive (VAR) Method. However, none of the three transformed data ...
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Performance and Analysis of Channel Estimation Techniques for LTE Downlink System under Fading with Mobility
... Least Square Error (LSE) and the Linear Minimum Mean Square Error (LMMSE) using QPSK and 16 QAM modulation techniques are studied and also studied the effect of channel length on the ...
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The Implementation of Forecasting Method by Incorporating Human Judgment
... 2) Forecasting results for the qualitative forecasting method: Moreover, for the analysis of the qualitative forecasting method, the Pure Judgment method is conducted for the whole demand data (28 periods). There is no ...
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Mean Square Error of Blind Channel Estimation Using a Constant Modulus Algorithm
... high error at the ...Least Square Estimation (LSE), Minimum Mean Square Error (MMSE) are based on single carrier system with pilot symbols for channel estimation to reduce the ...using ...
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Analysing the temporal dynamics of model performance for hydrological models
... Abstract. The temporal dynamics of hydrological model performance gives insights into errors that cannot be ob- tained from global performance measures assigning a single number to the fit of a simulated time series to ...
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Nonasymptotic bounds on the mean square error for MCMC estimates via renewal techniques
... Abstract The Nummellin’s split chain construction allows to decompose a Markov chain Monte Carlo (MCMC) trajectory into i.i.d. “excursions”. Regenerative MCMC algorithms based on this technique use a random number of ...
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Studies on amyloglucosidase from rhizopus delemar
... The standard error of a mean is obtained by dividing the square root of tho error variance standard deviation by the square root of the number of experimental points#... variance Is comp[r] ...
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Call option price function in Bernstein polynomial basis with no arbitrage inequality constraints
... We propose an efficient method for the construction of an arbitrage-free call option price function from observed call price quotes. The no-arbitrage theory of option pricing places various shape constraints on the option ...
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An Approach to Identify a Model for Efficient Prediction of Exchange Rates Using Setty Volatile Index (SVI)
... A NEURO-FUZZY system composed of an Adaptive NEURO FUZZY Inference System (ANFIS) controller used to control the stock market process model, also identified using an adaptive NEURO-FUZZY technique, is derived and ...
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Expert System-Based Predictive Cost Model for Building Works: Neural Network Approach
... Models for purpose of cost prediction in construction can be cost categorized into deductive and inductive model, in form and structure. Data can be analyzed using design variables with a view to developing a ...
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High Density Impulse Noise Removal in Color Images Using Region of Interest Median Controlled Adaptive Recursive Weighted Median Filter
... of Mean Square Error (MSE), Mean Absolute Error (MAE) and Peak Signal to Noise Ratio (PSNR) and it is compared with standard median filters, weighted median filters, center weighted ...
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