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Lasso regression prediction on test set is predicting towards the mean of the train set?

As an initial guess, overfitting the test data set probably isn't your problem. For linear models, Statistical Learning with Sparsity (SLS) notes on page 18: Somewhat miraculously, one can show that ...
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Transcribing from comments. For final populations there exists a correction factor which will probably useful for this purpose. $FPC=\sqrt{(N-n)/(N-1)}$, and the standard error formula becomes $SE=s/\... • 6,190 0 votes Estimate the mean vector and the covariance matrix using the simple returns The mean vector is the the vector of the individual means of the stocks. See example in R with two simple vectors below. Here I used colMeans to get the mean vector,... -1 votes Accepted Standard deviation on Bimodal data It would appear that Box Plots or Violin Plots would be better than just Mean and Standard Deviation on Bimodal data as they provide more details on the data distributions presented. en.wikipedia.org/... • 13 -1 votes Accepted Statistics to use on Bimodal data It would appear that Box Plots or Violin Plots would be better than just Mean and Standard Deviation as they provide more detail on the data distributions presented. en.wikipedia.org/wiki/Box_plot and ... • 13 9 votes Accepted Intuition for why mean of lognormal distribution depends on variance of normally distributed rv The intuition for this result comes from the fact that the exponential function is a strictly convex function. When you then impose a convex transformation on the random variable$X$, the positive ... • 94.5k 2 votes Mean and SD of daily log returns Analysis We can model the return on any given day$t$as a random variable$X_t.$This permits the log return after$n$trading days to be expressed as $$S_n = X_1 + X_2 + \cdots + X_n.$$ For ... • 287k 0 votes Simple question on statistical comparison of grand/pooled means I infer that your question is what can be done about this. You need to take advantage, carefully, of the numbers of relevant observations going into each "mean of means" and of the ... • 62.3k 3 votes Accepted Difference in means vs OLS regression coefficients Remember that linear regression involves a conditional expectation. It is very easy to show that, under assumptions, $$E(Y|x_1=A, x_2=B)=\beta_0 + \beta_1 A + \beta_{2} B + \beta_3 AB$$ Then:$$d_1 = ... • 15.7k 1 vote Accepted Confidence Intervals for More than One Variable? Could you use the generalised variance? Generalised variance is simply$\text{det}(\Sigma)$. Of course, we can map this to the standard error by dividing by$n = \text{sample size}$. We can illustrate ... • 2,275 1 vote Statistics to use on Bimodal data You are interested in the bimodality of the data, and no measure of central tendency conveys that. So choosing between a mean and a median is unlikely to help you. Instead, it makes sense to summarize ... • 1,711 3 votes Accepted Confidence in a range estimate, and +- 2sigma rule of thumb As @whuber wrote in the comments, what I am describing is a "Tolerance Interval" (see e.g., Prediction and Tolerance Intervals). If I understand things correctly so far, one connection with ... • 183 0 votes Accuracy failure example You can start with the Why is accuracy not the best measure for assessing classification models? thread. it gets the average correctness of the predictions and in some cases its result can be ... • 113k 0 votes Accepted How to change a variable equation so that one quantity impacts the outcome more than it currently is? If by "taking the mean of these two quantities" you mean you are calculating the Final Points as the mean of the Total Points and the Average Points, then the graph you've labelled "... • 476 1 vote How do I get the final mean value of imputed data? If you have$M$imputations done, then you want to create$M$analysis ready datasets, one for each of the multiple imputations. Each of the$M\$ datasets is based on processing the observed data (goes ...
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