Tagged Questions

1
vote
1answer
36 views

error in estimation with continuous data (New to Statistics)

Is there a way to correlate error in a fit (MSD) to the error of the a calculation performed with the parameters associated with the fit? My specific problem is dealing with spectroscopic data. I ...
1
vote
1answer
47 views

Cauchy M estimator of regression in R

Was wondering if anyone knows of an R package to estimate the Cauchy-M estimator of regression (see for example the end of this section, but with simultaneous estimation of the scale parameter as in ...
0
votes
1answer
88 views

Dropping a variable from a multiple linear regression model, causes another to become non-significant [duplicate]

Suppose we have a regression model that measures college Grade Point Averages. The variables that we are using are hsize (the size of the graduating class in ...
0
votes
2answers
43 views

difference in training and testing procedure of model

Can anyone please tell me the difference in training and testing of a model. I have developed 5/6 different single pass online learning algorithm (ets, ets+, evolving fuzzy modelling, SOFNN, ...
1
vote
0answers
65 views

Restricted least squares: condition is that the parameter vector is greater or equal zero

I only found a example in which the constraind on $\beta_i, \ i=1,...k$ would make them sum up to one as well introduces a constraind such that all $\beta_i$ are all greater or equal zero. Since I ...
0
votes
1answer
69 views

Parameter learning of Markov random field

Given a Markov random field $\mathcal{G} = (\mathcal{V},\mathcal{E})$, the corresponding density function to which is expressed by $f(x) \propto \prod_{x\in\mathcal{V}} \psi_u(x) ...
0
votes
0answers
32 views

Estimable function of OLS parameters can be shown by inner product with Null space?

I am in a advanced linear models class, and we are currently covering estimable functions. The criterion that we have for an estimable function is that for any $a^T\beta$ there exists an unbiased ...
2
votes
1answer
77 views

Parameter estimate for linear regression with regularization

For given cost function $S(\beta) = (Y - X \beta)^T(Y - X \beta) + \lambda \beta^T \beta$, where $\lambda$ is regularization parameter, the $\beta$ that minimizes the given cost function is $\beta = ...
-1
votes
1answer
64 views

How to show what is going on if we drop significiant variable in logit model?

How to show what is going on if we drop significant variable in logit model ? Bias and heteroskedasticity should emerge. But what is the framework for showing such behaviours in econometric models ?
0
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0answers
36 views

Show that the slope estimate can be written as a sum

I am stuck on a homework question for a graduate level linear models class. The question is to show that for simple linear regression the slope estimate $ \hat{\beta_1} = ...
1
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0answers
114 views

Discussion about proxy- and instrument variables and endogeneity in the context of a multi equation model

Assume two equations $Y_1 = X_1\beta_1 + X_2\beta_2 + U_1$ $Y_2 = X_1\alpha_1 + X_2\alpha_2 + U_2$ Further assume that $ \ U_1 = X_4 + E_1$ and $U_2 = X_4 + E_2$ with $ \ corr(Y_1,X_4)\ne 0, \ \ ...
1
vote
1answer
906 views

How to estimate GARCH in R? (Exogenous variables in mean equation)

What I'm trying to do is estimate the following GARCH(1,1) model in R with the garchFit function from the fGarch package: ...
2
votes
1answer
115 views

Compare two teams who competed in slightly different competitions

Two running teams take part in competitions on the same course, but their competitions differ because of the number of teams entered in each competition. Results are based solely on position - times ...
2
votes
1answer
96 views

Help computing asymptotic variance of a weird first difference estimator in a fixed effects model

I'm working on an econometrics problem set, and I'm having some major problems computing asymptotic variance for this estimator. I'm considering a fixed-effects model $$ Y_{it} = \beta_1 X_{it} + ...
0
votes
1answer
403 views

Similarities and differences between regression and estimation

What's the similarities and differences between parametric regression analysis and estimation theory? I notice that they are both about parameter estimation, and both require some models for ...
0
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0answers
58 views

When are two trend estimates identical within errors?

Given two linear trend estimates $m_1$, $m_2$, with their respective errors $e_1$, $e_2$, how can I determine if the two trend estimates are the same within errors? EDIT: Both estimates are derived ...
5
votes
3answers
289 views

Can I use a variable which has a non-linear relationship to the dependent variable in logistic regression?

Let's say I am building a logistic regression model where the dependent variable is binary and can take the values $0$ or $1$. Let the independent variables be $x_1, x_2, ..., x_m$ - there are $m$ ...
0
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2answers
103 views

Estimating standard error of related regressions in R

I am working with regressions to understand the price creation of certain future contracts for commodities and try to explain it with other commodity pricese. These future contracts have different ...
3
votes
1answer
258 views

Standard error of parameter estimates in regularized regression

In a regularized linear regression model (e.g., ridge regression, lasso, etc.), what is the best way to obtain standard errors for parameter estimates? If cross-validation is used, is it ...
2
votes
0answers
157 views

Nonlinear estimates of regression coefficients?

About simple regression: It's well known that usual OLS estimators of $β_{0}$ and $β_{1}$ have minimum variance of all unbiased linear estimators. I wonder if there are popular biased linear ...
2
votes
1answer
168 views

Adjusting for zero mean (standardizing) in a multiple regression model

A friend of mine was telling me today about the need to mean adjust input variables to zero in order to "get rid of implicit intercept" or scale terms in the slope coefficients and to make their ...
1
vote
3answers
754 views

Standard error of a ratio

I have a linear regression and two estimates, say A and B and their standard errors. I need to find the standard error of a ratio A/B [or A/(1-B)]. I guess the main problem is that I don't know the ...
6
votes
1answer
2k views

Mean squared error vs. mean squared prediction error

What is the semantic difference between Mean Squared Error (MSE) and Mean Squared Prediction Error (MSPE)?
1
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0answers
88 views

Why is an L1-estimator a special case of an M-estimator?

An L1 estimator minimizes the absolute difference between $Y$ and $x\beta$, whereas an M-estimator minimizes some function of the residuals. But the objective function needs to be differentiable ...
0
votes
0answers
146 views

How to develop intuition about parameter estimation in AR-GARCH model?

consider a typical ar-garch model: y = sum( bi*xi ) + epsilon, epsilon~garch(p,q) In such case a typical textbook says that we should first estimate all bi's ...
1
vote
2answers
1k views

How to calculate Theil–Sen estimator in R?

I found a useful method named "Theil–Sen estimator" on wikipedia: here This method seems to be covered with mblm function on the mblm library. I just downloaded it to do some tests. I have one ...
1
vote
0answers
181 views

Confusion in MLE and EM [closed]

I was trying to read through Maximum Likelihood Estimation(MLE) and Expectation and Maximization(EM) algorithm. But while reading them, I got two interpretations. I am trying to post my questions, ...
5
votes
1answer
162 views

Estimating speed from position updates with uncertain time intervals

I have 2 alternative methods to solve a problem, and I was just wondering what people who know the math better than I think, and if there is a better method to use for this type of problem. The ...
9
votes
1answer
285 views

Methods for fitting a “simple” measurement error model

I am looking for methods which can be used to estimate the "OLS" measurement error model. $$y_{i}=Y_{i}+e_{y,i}$$ $$x_{i}=X_{i}+e_{x,i}$$ $$Y_{i}=\alpha + \beta X_{i}$$ Where the errors are ...
4
votes
1answer
382 views

How to apply a Kalman filter to use both previous and future measurements of a random variable?

I'm trying to estimate the state of a Gaussian random walk with central tendency based on time series measurements with varying uncertainties. My random variable has the following form: $ \frac{d ...
2
votes
3answers
311 views

Effect of missing data and outliers on least square estimation

Why is it that "missing data" and "outliers" can affect the performance of least square estimation?