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The ability to look beneath the surface is based on data dimensions an aspect of how data is recorded, stored, and accessed in Google Analytics. Just like the metrics we cover in 6, dimensions are fundamental to the structure of the data collected by Google Analytics. Think of a dimension as a category for information, while a metric is a fact about information, and the thing in question is the information itself. Review 4 for details on how data is collected and processed into dimensions. At a fundamental level, understanding how to use dimensions in Google Analytics effectively requires understanding the nature of multidimensional analysis. Looking at data in 2D versus 3D reveals a different view of the same thing. Consider a page on your Web site let s say the home page. The page is the object and it has a given name, homepage. There are facts about this page, such as how many times it was viewed (metric: Pageviews) and how many times it was the last pageviewed during a visit (metric: Exits). There are also



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In the 1940s when little was known about how human brain processes information, McCulloch and Pitts proposed a simple neural network (NN) model It allows for massive parallel processing of neural input in the brain at multiple layers and feedbacks leading to nonlinear output Mathematically, NN involves weighted sums of inputs in layers with several nodes linked to an intermediate hidden layer and then output to fewer nodes Biologists have since learned that human neurons are far more complex However, the mathematical model has been applied with modi cations to nonparametric estimation, medical diagnoses, and different kinds of learning Financial economists and those in related elds may simply think of feed-forward NN as a method for generalizing linear regression functions (Kuan and White, 1994) In early versions of neural network models the link functions similar to h in (65.





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i=1 Note that the researcher fully controls the simulation size, n, and therefore the simulation accuracy of the estimate Also, because the central limit theorem applies here as long as Var(I [A, B]) is nite, credible intervals can eas ily be calculated by [95%lower , 95%upper ] = [I [A, B] 196 Var(I [A, B]), I [A, B] + 196 Var(I [A, B])], or simply by reporting the 0025 and 0075 empirical quantiles from the simulation sample Although a great deal of the literature on Monte Carlo methods is concerned with variance reduction, readers often have no idea what an individual study has done with regard to simulation error and other concerns (Ripley 1987) Kennedy and Gentle (1980, p.

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5) described earlier were heaviside or unit step function: h(m) = 1 if m 0 and h(m) = 0 if m < 0 The neuron is turned on when h(m) = 1, say, and remains off otherwise More generally it is a threshold function: h(m) = 1 if m m0 and h(m) = 0 if m < m0, where m0 is the threshold value For further generality the link function h(m) is chosen to be smoothly increasing CDF as in (655) Neural networks, inputs are signals xi, i = 1, , p, assembled in a column vector x When such an x vector is augmented by inserting the number 1 at the start, we denote it by x with p + 1 elements The signal goes to output units yj, j = 1, , r xigji reaches the jth output.

(5.12)

If xi = 1 for the intercept, the output is g0j We assemble p + 1 coef cients gj0 to gjp in a (p + 1) 1 column vector denoted by gj Since both gj and x are (p + 1) 1 vectors, (x gj) is a scalar, where the prime denotes a transpose There are r such scalars comprising r equations of so-called seemingly unrelated regressions (SUR) model Econometrics texts describe the SUR model as one of the generalizations of single equation regression model Now let us introduce one hidden layer of activations with k = 1, , q additional sets of parameters assembled in bkj, a q r matrix This matrix links the q hidden layers to r outputs In Figure 651 we illustrate a [4, 2, 3] neural network with four input nodes, two hidden nodes, and three output nodes Figure 65.

235) note that Frequently one sees in articles in the statistics journals reports of Monte Carlo studies that fail to supply any details of the study; only the results are given Since the results are realizations of pseudorandom processes, however, the reader of the article may be left wondering about the magnitude of the pseudo-standard errors and, indeed, about the effect of the pseudo nature of the process itself This statement is certainly no less accurate with regard to MC studies in social science journals, and the situation may even be worse 58 SO WHAT CAN BE DONE We have identi ed several potential pitfalls in MCMC simulation that are substantially understudied This is the rst systematic look at the effects of numerical.

" ' where the values X d , X d and X d are expressed as percentages (see section 4.2.1.3).

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