DISCUSSION OF: TREELETS—AN ADAPTIVE MULTI
... in (1) is not identifiable, as in known in factor analysis. Consider, for instance, Example 2. If we redefine Uj∗ = Uj , j = 1, 2, v3∗ = c1 v1 + c2 v2 , and U3∗ = 0, we are at the same covariance matrix as in (19) with only two nonoverlapping blocks. The treelets transform evidently gives a decompos ...
... in (1) is not identifiable, as in known in factor analysis. Consider, for instance, Example 2. If we redefine Uj∗ = Uj , j = 1, 2, v3∗ = c1 v1 + c2 v2 , and U3∗ = 0, we are at the same covariance matrix as in (19) with only two nonoverlapping blocks. The treelets transform evidently gives a decompos ...
ISMIR2006_ResponseRates_Queries_poster - Music
... related to a query being answered. Analysis: First, we examine how the proportion of queries answered varies over time. Then we compare selected features of answered and unanswered queries, namely the price offered for the answer and the length of the query, in order to understand if these variables ...
... related to a query being answered. Analysis: First, we examine how the proportion of queries answered varies over time. Then we compare selected features of answered and unanswered queries, namely the price offered for the answer and the length of the query, in order to understand if these variables ...
Removal Efficiency in Industrial Scale Liquid Jet K. S. Agrawal
... ) and the liquid concentration is more significant between two. It may be observed that fitted models do not contain the independent term ( Ψ ). This implies that the removal efficiency ( ) is a function of the factors considered only. ...
... ) and the liquid concentration is more significant between two. It may be observed that fitted models do not contain the independent term ( Ψ ). This implies that the removal efficiency ( ) is a function of the factors considered only. ...
Two-Stage Estimation of Non-Recursive Choice Models
... 1989). This was done by setting each variable at the sample mean, calculating the probability that y1 = 1, then increasing the value of the variable by 0.5, and calculating the probability that y1 = 1 again. We performed this procedure using both the estimated coecients from the 10,000 observation ...
... 1989). This was done by setting each variable at the sample mean, calculating the probability that y1 = 1, then increasing the value of the variable by 0.5, and calculating the probability that y1 = 1 again. We performed this procedure using both the estimated coecients from the 10,000 observation ...
B632_06lect13
... • “Given your own knowledge of radiation effects on humans and other organisms, which of the above hypothesized relationships do you think is most likely correct?” • “On a scale where zero means not at all certain, and ten means completely certain, how certain are you that the relationship you ident ...
... • “Given your own knowledge of radiation effects on humans and other organisms, which of the above hypothesized relationships do you think is most likely correct?” • “On a scale where zero means not at all certain, and ten means completely certain, how certain are you that the relationship you ident ...
Statistical Downscaling of Daily Temperature in Central Europe
... be a useful exercise. In the first study of this kind related to daily temperature, Winkler et al. (1997) concentrated on a sensitivity of downscaling output to a definition of seasons and the form of the transfer function, but relied on circulation predictors only (sea level pressure and 500-hPa he ...
... be a useful exercise. In the first study of this kind related to daily temperature, Winkler et al. (1997) concentrated on a sensitivity of downscaling output to a definition of seasons and the form of the transfer function, but relied on circulation predictors only (sea level pressure and 500-hPa he ...
Coefficient of determination
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced R squared, is a number that indicates how well data fit a statistical model – sometimes simply a line or a curve. An R2 of 1 indicates that the regression line perfectly fits the data, while an R2 of 0 indicates that the line does not fit the data at all. This latter can be because the data is utterly non-linear, or because it is random.It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model (pp. 187, 287).There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. In this case, if an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the outcomes and their predicted values. If additional explanators are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination ranges from 0 to 1.Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. Additionally, negative values of R2 may occur when fitting non-linear functions to data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.