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Jul
27
comment combining multiple regression outputs
If neither $\beta$ nor $\alpha$ depends on $t$ then, in theory at least, all results should be equal to the true approximation. The reason is because you've effectively drawn a random sample for each regression. In that case, all you need to do to recover $\beta^*$ is take the average and the only weighting would likely be a function of the sample sizes.
Jul
16
comment Approximate/Find Function
Kernel Regression
Jul
9
comment Underlying utility function behind a linear two-product demand curve
My guess is that in the second case you automatically assumed the budget constraint is binding, but that in fact depends on the value of $m$.
Jul
7
comment Identification of non-linear functions:polynomial+exponential
Maybe you wrote something wrong in the question, but it seems to me the answer is clearly $\alpha_2 = \alpha_1 = \alpha_0\ = \beta=0$ Or is that supposed to be a $Y$ on the RHS
Jul
7
comment Comparing Percentiles of 2 Samples Drawn from the Same Distribution
Interesting, thanks! Just so I'm clear, the discontinuities in the graph occur at the points where $np$ is a whole number, because at the next value for $n$ we round all the way up.
Jul
7
accepted Comparing Percentiles of 2 Samples Drawn from the Same Distribution
Jul
7
asked Comparing Percentiles of 2 Samples Drawn from the Same Distribution
Jul
2
answered Significance of dummy variables in probit regression
Jul
1
comment Bayesian Updating - plug in previous posterior for prior?
Just to be clear, is the new information the entire sequence or just the terms indexed by $n+1$, e.g. $a_{n+1}$ and $b_{n+1}$
Jul
1
comment Least squares with known error in y
Measurement error in $y$ variables does not bias regression coefficients (i.e. in $x$). So, at least in theory, it should not make a difference. If that result is affected by a small sample size, I can't say for sure.
Jun
28
answered In a simple regression model estimated using OLS, the covariance between the estimated errors and regressors is zero by construction
Jun
28
comment In a simple regression model estimated using OLS, the covariance between the estimated errors and regressors is zero by construction
Yes, its true. Intercepts do not matter.
Jun
28
comment Polynomial least squares fit — restrictions on order?
The example in the link you provided is solving a simple regression with a constant term and a variable x. ($y=a+bx$) This is a regression with 2 independent variables. That's why the matrix is 2x2. (Using the formula in my previous comment, X'X is a 2 x 2 matrix). But, you want to know (or at least I think you want to know) largest order you can include in a regression of y on x, $x^2$, $x^3$, and so forth. This will require solving higher order matrices. Edit: Here's a better link. mathworld.wolfram.com/LeastSquaresFittingPolynomial.html
Jun
28
comment Polynomial least squares fit — restrictions on order?
The standard solution in a linear regression is $(X'X)^{-1}X'y$ where X is an $ N x K$ matrix with N observations and K parameters. In order to be valid the matrix $X'X$ must be invertible. This requires , by definition, that $N \geq K$.
Jun
27
revised Polynomial least squares fit — restrictions on order?
added 148 characters in body
Jun
27
answered Polynomial least squares fit — restrictions on order?
Jun
26
comment Non-linear regression fit
You can not linearize it. Not without knowing A. The closest thing you could do would be to do your method at several different values of A. And then pick the set of values with the smallest SSE.
Jun
25
comment Estimating grader bias/variance and MLE test scores given multiple graders assigned to grade each test
Yes, thats true. You might be able to use the average test score to get an additional moment. It makes sense you need to make a normalization. You need to establish some sort of baseline. For example, suppose all children received the same mark. How would you know whether all graders were biased or unbiased unless you had something to compare it to?
Jun
25
revised Estimating grader bias/variance and MLE test scores given multiple graders assigned to grade each test
added 6 characters in body
Jun
24
answered Estimating grader bias/variance and MLE test scores given multiple graders assigned to grade each test