# Tag Info

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Closing the loop on this for archiving purposes.... A friend explained this to me offline. The answer is YES. Just as the mean of multiple random samples converges on the true population mean (even if the data are highly non-normal), model parameters fit to multiple random subsets will converge on the true model parameters for the population. In fact, ...

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It's not bad and actually may improve quality quite a lot if you have a lot of unlabeled data (i.e. $X$'s without $Y$'s) and some labeled data: you train a model with labeled data, label unlabeled with your model and retrain the model. This falls into the class of so-called semi-supervised learning methods: ...

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The flat maximum effect is simply that models with very different coefficients can have very similar performance on a particular prediction or classification task. When this happens it makes interpreting model coefficients difficult. One example of where this occurs is in linear regression with multicolinearity of the predictor variables. As another ...

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genetic algorithms were used to lower the prime gap to 4680 in the recent Zhang twin primes proof breakthrough & associated polymath project. the bound has been lowered by other methods but it shows some potential for machine-learning approaches in this or related areas. they can be used to devise/optimize effective "combs" or basically sieves for ...

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One solution would be to create a location variable that has four levels: Urban-urban, urban-rural, rural-urban and rural-rural. Then this would only get an i subscript.

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The paper you are reading is implicitly using $\alpha_i$ and $\beta_i$ to refer to the attack and defense parameters as described by Maher(1982). The main difference is that Maher uses four parameters for each team (home attack, home defense, away attack and away defense) while Dixon and Coles, use an attack and defense parameter and another parameter to ...

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Just some strayed thoughts. 1) You fit a multiple regression to examine the effect of a particular variable a worker in another department is interested in. The variable comes back insignificant, but your co-worker says that this is impossible as it is known to have an effect. What would you do? Many reasons could have caused this: The study is ...

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1) This is a multiple regression. Presumably there are other variables. It may be the case that the specific variable in question has an effect on the response, but in the presence of the other variables its effect is not significant. I'd use partial least squares pairing the variable in question with the other variables to determine which other variables ...

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1) If you mean that the co-worker knows the variable has a large (or important) effect on the response and yet your analysis did not detect this important effect then you need to first ensure that your analysis methods are not in error. If the methods are sound, then you should ask and have answered why the data you were given is not in harmony with your ...

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1) Why would you test it in the first place if you already knew that there was a relationship? Therefore, there must be something unique about what you are investigating. It could be that there is another predictor variable in the model that is making the effect of the predictor of interest non-significant. It could simply be Type II error. What would I do? ...

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(1) This doesn't quite make sense. "What would you do" is very general. Often variables that are known to be significant/related to the outcome are included in a regression model even if they are found not to be significant (to increase confidence in that you are providing unbiased estimates for the other coefficients). So one answer would be you keep the ...

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Make two columns in Excel: A is CDC data, B is Google Trends data. Then you will make two new columns. In cell C2 put "=A2-A1" and copy/paste this equation down to one row past the data in column A. Similarly put "=B2-B1" in cell D2 and copy/paste it. Columns C and D are your rates of change. Next plot column C vs column D and fit a line to perform linear ...

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If you don't have a very large sample, the jarque.bera test will almost always reject normality of the residuals for a VAR. I suggests you plot your residuals in a histogram and a qqplot to see for yourself if it is reasonable to believe that the residuals are behaving like white noise.

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You could consider using a vector autoregressive model, to model each country individually. The model would look something like this, $$\mathbf{y}_t = c+A_i\mathbf{y}_{t-1}+\cdots+A_p+\mathbf{y}_{t-p}+u_t,$$ where $\mathbf{y}_t=(y_{1,t},y_{2,t},\dots,y_{n,t})'$ and $u_t$ is $IIDN(0,\sigma)$ So you would estimate one vector autoregressive model for each ...

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If your goal is to quantify parameter dependencies have you considered a sensitivity analysis? This report may be of use to you.

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