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Topic: Mean squared error

	Mean squared error - Wikipedia, the free encyclopedia
		In statistics the mean squared error of an estimator T of an unobservable parameter θ is
		The "error" is the amount by which the estimator differs from the quantity to be estimated.
		The smaller variance compensates somewhat for the bias, so that the mean squared error of the biased estimator is slightly smaller than that of the unbiased estimator.
	en.wikipedia.org /wiki/Mean_squared_error (203 words)

	3.2 Error Measurements
		This error output, taken from the output from the Naive Bayes learner model, is typical of error output for models that have numerical output attributes.
		Mean absolue error is the average of the difference between predicted and actual value in all test cases; it is the average prediction error.
		Relative squared error is the total squared error made relative to what the error would have been if the prediction had been the average of the absolute value.
	grb.mnsu.edu /grbts/doc/manual/Error_Measurements.html (691 words)

	Root Sum Squared -
		and the hypotenuse is the square root of the sum of 1 squared + z squared.
		the square root of the sum of the squares...
		The sum of squared deviations is: 9 + 1 + 1...
	root.faasv.com /index.php?k=root-sum-squared (1083 words)

	[No title] (Site not responding. Last check: 2007-10-13)
		The estimation error epsilon is the difference between the estimated parameter and the parameter itself.
		The mean-squared error is given by the mean squared error matrix Eepsilon^T epsilon.
		Mean squared error matrix +------------------------------------------------------------ The Mean squared error matrix is defined as Eepsilon^T epsilon, where epsilon is the difference between the estimated parameter and the parameter itself.
	www.math.harvard.edu /~knill/sofia/data/statistics.txt (413 words)

	How to compare models
		The mean absolute error (MAE) is also measured in the same units as the original data, and is usually similar in magnitude to, but slightly smaller than, the root mean squared error.
		If an occasional large error is not a problem in your decision situation (e.g., if the true cost of an error is roughly proportional to the size of the error, not the square of the error), then the MAE or MAPE may be a more relevant criterion.
		This means converting the forecasts of one model to the same units as those of the other by unlogging or undeflating (or whatever), then subtracting those forecasts from actual values to obtain errors in comparable units, then computing statistics of those errors.
	www.duke.edu /~rnau/compare.htm (2470 words)

	Linear Estimators (Site not responding. Last check: 2007-10-13)
		The linear estimator is that particular linear transformation that yields an estimation error orthogonal to all linear transformations of the data.
		The orthogonality of the error to all linear transformations is termed the universality constraint.
		The second term reduces this maximum error and represents the degree to which the estimate and the parameter agree on the average.
	cnx.rice.edu /content/m11276/latest (1052 words)

	Chapter 5
		Mean squared error is increased when frequency-domain blocking is applied to the extrapolation system.
		The increase in error when using the FDB process is mainly due to the fact that the power density spectrum used as a weight in the extrapolation method is affected by blocking in some instances.
		Error does not greatly fluctuate when music is used as the input due to the fact that music has a semi-flat spectral density and has a pseudo-random nature.
	www.music.miami.edu /programs/mue/research/asouppa/chapter5.htm (3168 words)

	Untitled Document
		Mean Squared Error (M.S.E) Momentum rate : 0.01
		Mean MSE (training set) graph for each momentum value with standard deviation ranges.
		It prevents large oscillations from occuring which can lead to slow convergence when the error gradient is steep.
	www.cs.bham.ac.uk /~pxt/NC/ASSIGNMENT/MICHAEL/momentum.html (163 words)

	What are Mean Squared Error and Root Mean Squared Error? - Vernier Tech Info Library #1014
		That is, for every data point, you take the distance vertically from the point to the corresponding point on the curve fit (the error), and square the value.
		The smaller the Mean Squared Error, the closer the fit is to the data.
		The MSE has the units squared of whatever is plotted on the vertical axis.
	www.vernier.com /til/1014.html (195 words)

	Mean squared error of empirical predictor, Kalyan Das, Jiming Jiang, J. N. K. Rao
		Mean squared error of empirical predictor, Kalyan Das, Jiming Jiang, J. Rao
		In this paper, we consider mean squared errors (MSE) of empirical predictors under a general setup, where ML or REML estimators are used for the second stage.
		The general results are applied to mixed linear models to obtain a second-order approximation to the MSE of the empirical best linear unbiased predictor (EBLUP) of a linear mixed effect and an estimator of the MSE of EBLUP whose bias is correct to second order.
	projecteuclid.org /getRecord?id=euclid.aos/1083178948 (259 words)

	[No title]
		MSE is the sum of the squares of the differences between between real and predicted values divided by the number of test items.
		This doesn't mean much, only that in these Boolean problems, the performance of C5.0boost clusters closely around a mean, unlike in the ucis.
		So the error reported by the 10 fold CV was the mean of the MAD for the 10 folds.
	www.cs.bris.ac.uk /~hilanb/metal/regression.report (5187 words)

	QccDatasetMSE.3 (Site not responding. Last check: 2007-10-13)
		QccDatasetMSE() calculates the mean squared error (MSE) between the vector arrays of two datasets.
		The square of this norm is calculated to give the squared distance, or squared error, between the two vectors.
		Finally, the squared error for all vectors is summed and divided by the block size to give the mean squared error.
	qccpack.sourceforge.net /Documentation/QccDatasetMSE.3.html (118 words)

	msereg (Neural Network Toolbox)
		It measures network performance as the weight sum of two factors: the mean squared error and the mean squared weight and bias values.
		The error is calculated by subtracting the output
		Then the mean squared error is calculated using a ratio of 20/(20+1).
	www.oulu.fi /tietohallinto/ohjeet/unix/matlab/matlab-6.5.1/help/toolbox/nnet/msereg.html (158 words)

	Why minimising in the mean-error sense. (Site not responding. Last check: 2007-10-13)
		error signal, and total squared error is the energy of the error signal,
		squared error, unless we have a reason to set a different goal.
		The mathematical reason of the use of the mean square error term is that
	www.dsprelated.com /showmessage/16859/1.php (1882 words)

	Journal of the American Statistical Association: The mean squared error of small area predictors constructed with ... (Site not responding. Last check: 2007-10-13)
		The mean squared error of small area predictors constructed with estimated area variances.
		In the small area estimation literature, the sampling error variances are customarily assumed to be known or to depend on a finite number of parameters.
		An approximation for the mean squared error (MSE) of the EBLUP that recognizes the impact on the predictors of estimation of the variance components is derived.
	highbeam.com /library/doc0.asp?docid=1G1:111300297&refid=ink_tptd_mag (247 words)

	Glossary of research economics
		Example: Suppose average market return to a stock was 10% for some calendar year, meaning stocks overall were 10% higher at the end of the year than at the beginning, and suppose that stock S had risen 12% in that period.
		Mean is r, variance is 2r, pdf and cdf is difficult to express in html, and moment-generating function (mgf) is (1-2t)
		is defined to be the square of the correlation between the measured scale and the underlying factor the scale was supposed to measure.
	www.econterms.com /econtent.html (14590 words)

	Cubic - maths interpolation - C / C++ Numerical Component
		It is the smoothest of all possible interpolating curves in the sense that it minimizes the integral of the square of the second derivative.
		You may notice the root mean squared error in each of the cases.
		This means that none of the abscissas in the interval from X[N - 2] to X[N - 1] are valid.
	www.codecogs.com /pages/pagegen.php?id=1126513006-378 (348 words)

	appendixb.htm
		The mean squared error has a useful decomposition into two components the variance of the estimator and the square of its bias:
		It is well-known that the residuals are uncorrelated with the estimators of the regression coefficients (and independent in the case of normal errors).
		This means that the estimator converges to the true value of the parameter as the sample size (and hence the information) approaches infinity.
	www.math.uwaterloo.ca /~dlmcleis/book/appendixb.htm (4658 words)

	[No title] (Site not responding. Last check: 2007-10-13)
		The resulting mean-squared error for this forecasting technique on the data is: 88,571.99.
		The resulting mean-squared error for this forecasting technique on the data is 146,634.17.
		The resulting mean-squared error for this forecasting technique on the data is 82,808.44.
	cmsu2.cmsu.edu /public/classes/crooker/Fin4800/study_guide_2004_midterm_key.doc (1060 words)

	[No title] (Site not responding. Last check: 2007-10-13)
		It measures % network performance as the weight sum of two factors: % the mean squared error and the mean squared weights and biases.
		% and returns the sum of mean squared errors (times PP.ratio) with the % mean squared weight and bias values (times 1-PP.ratio).
		% % net = newff([-2 2],[4 1],{'tansig','purelin'},'trainlm','learngdm','msereg'); % % Here the network is given a batch of inputs P. The error is % calculated by subtracting the output A from target T. Then the % mean squared error is calculated using a ratio of 20/(20+1).
	www.clemson.edu /cle4_share/CWE/COES0915_CLUG/REFERENCE/matlabr14/toolbox/nnet/nnet/msereg.m (414 words)

	Blind Deconvolution (Site not responding. Last check: 2007-10-13)
		The first is the Wiener Filter, which exhibits the optimal tradeoff (in the MSE sense) between inverse filtering and noise smoothing.
		Suu is estimated as the magnitude squared of the Fourier Transform of the input image (lenna.256).
		We estimated the image spectral density as the magnitude squared of the Fourier Transform of the degraded image (rather than of the original image, as before).
	www.owlnet.rice.edu /~elec539/Projects99/BACH/proj2/blind/bd.html (1486 words)

	Citebase - Nonparametric Estimation of Mean-Squared Prediction Error in Nested-Error Regression Models (Site not responding. Last check: 2007-10-13)
		Prediction is usually the main goal of such analyses, and mean-squared prediction error is the main way in which prediction performance is measured.
		In this paper we suggest a new approach to estimating mean-squared prediction error.
		We introduce a matched-moment, double-bootstrap algorithm, enabling the notorious under-estimation of the naive mean-squared error estimator to be substantially reduced.
	citebase.eprints.org /cgi-bin/citations?id=oai:arXiv.org:math/0509493 (299 words)

	UvA DARE
		On the expansion of the mean integrated squared error of a kernel density estimator
		We give a new proof of the mean integrated squared error expansion for non smooth densities of Van Eeden.
		The proof exploits the Fourier representation of the mean integrated squared error.
	dare.uva.nl /record/114555 (81 words)

	The page cannot be found (Site not responding. Last check: 2007-10-13)
		HTTP Error 404 - File or directory not found.
		Go to Microsoft Product Support Services and perform a title search for the words HTTP and 404.
		Open IIS Help, which is accessible in IIS Manager (inetmgr), and search for topics titled Web Site Setup, Common Administrative Tasks, and About Custom Error Messages.
	www.absoluteastronomy.com /encyclopedia/m/me/mean_squared_error.htm (121 words)

	Mean Squared Error (Site not responding. Last check: 2007-10-13)
		Prediction of quality derived from the result of computing the mean of the squares of the differences
		As the result approaches zero, the more identical are the original and captured image
		Peak Signal-to-Noise Ratio is a variation of MSE:
	www.irt.de /IRT/publikationen/asf/tektronix/sld013.htm (39 words)

	Variability - Free Statistics and Forecasting Software (Calculators)
		Mean Absolute Differences between all Pairs of Observations
		We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice.
		or representations as to the accuracy or completeness of such information (or software), and it assumes no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications.
	www.wessa.net /varia.wasp (445 words)

	Furniture insurance estimator, more information about estimator (Site not responding. Last check: 2007-10-13)
		However, if an instrument is available, consistent estimates may still be obtained.
		Truncated mean From Sterwiki A truncated mean is a statistical measure of central tendency, much like the mean and median.
		It involves discarding given parts of a probability distribution or sample at the top or the bottom end, and typically involves discarding an equal amount at each end.
	www.furniture-insurance.com /estimator.html (289 words)

	Descriptive Statistics - Variability - Mean Squared Error (MSE)
		Descriptive Statistics - Variability - Mean Squared Error (MSE)
		ORDE uses reasonable efforts to include accurate and timely information and periodically updates the information without notice.
		or representations as to the accuracy or completeness of such information, and it assumes no liability or responsibility for errors or omissions in the content of this web site.
	www.xycoon.com /mse.htm (332 words)

	Citebase - Mean squared error of empirical predictor (Site not responding. Last check: 2007-10-13)
		Citebase - Mean squared error of empirical predictor
		@article{das-2004-32, author = {Kalyan Das and Jiming Jiang and J N K Rao}, title = {Mean squared error of empirical predictor}, journal = {ANNALS OF STATISTICS}, volume = {32}, pages = {818}, url = {doi:10.1214/009053604000000201}, year = {2004} }
		MR625025 Kackar, R. and Harville, D. Approximations for standard errors of esti-
	citebase.eprints.org /cgi-bin/citations?id=oai:arXiv.org:math/0406455 (380 words)

	Professor White's Publications
		White and M. Stinchcombe: "Adaptive Efficient Weighted Least Squares with Dependent Observations," in W. Stahel and S. Weisberg, eds., Directions in Robust Statistics and Diagnostics, IMA Volumes in Mathematics and Its Applications.
		S. Goncalves and H. White: "The Bootstrap of the Mean for Dependent Heterogenous Arrays,", Econometric Theory, 18, 1367-1384 (2002).
		Plutowski, S. Sakata and H. White: "Cross-Validation Estimates Integrated Mean Squared Error," in J. Cowan, G. Tesauro, and J. Alspector, eds., Advances in Neural Information Processing Systems 6.
	weber.ucsd.edu /~mbacci/white/pubs.html (2882 words)

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