Refers to the variables used in a model to predict a response. This tag can also be used for $X$ variables in explanatory & descriptive modeling, not just predictive modeling. This same construct goes by many names in different contexts, including: independent variable, explanatory variable, ...
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34 views
Simple lmer model specification help needed
We would like to analyze some of our fixation probability data with the lme framework. This is binomial data collected over several hundred trials with several dozen subjects. We want to asses whether ...
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0answers
23 views
Looking for special case for EKF and PEM parameter identification
In system identification is very much of methods. I was discusing extended kalman filter (EKF) used for parameter identification and prediction error method (PEM). For ARMAX model this two methods ...
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2answers
157 views
What statistics should I use for evaluating the accuracy of predictions?
I have two variables representing 1) players' predicted fantasy football points and 2) players' actual fantasy football points scored. What statistics are best for assessing the accuracy of the ...
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1answer
54 views
Strongest predictor of outcome
I am doing a study that is on 5 biomarkers a, b, c, d, e which are continuous variables. Having high a, b, c is bad and low d, e is bad for the body -- it causes bad outcomes.
Now, I collected data ...
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1answer
89 views
Can we use as predictor a variable that was used in the calculation of the dependent (a ratio)?
I wonder if someone could give me some advice on the problem of using ratios as a dependent variable in a Generalized Linear Model.
I'm having the following problem:
I have a variable referring to ...
2
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2answers
57 views
Can you use proportions as a covariate in a Cox proportional hazards model?
In R's survival::coxph function, can I mix a covariate representing proportions (in the range 0.0-0.5) with an integer covariate (in the range 1-15), or should I ...
3
votes
1answer
303 views
How should I handle a left censored predictor variable in multiple regression?
I have a dataset (N=350) for which I would like to regress a neuropsychological test score (continuous) on age, education, symptom severity (continuous), and diagnosis (binary). Symptom severity is ...
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1answer
162 views
Interpret significant predictor in non-significant regression? [duplicate]
Possible Duplicate:
How can a regression be significant yet all predictors be non-significant?
In a simple linear regression with multiple predictors, is it valid to interpret the ...
3
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0answers
164 views
Rule of thumb - number of predictors - Poisson regression rates
I am interested in estimating a Poisson regression for mortality rates, with number of deaths as the dependent variable and log(population size) as the offset. I have 50 observations (states).
I am ...
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1answer
128 views
Comparing continuous predictors for a dichotomous variable
I have two continuous predictor variables to predict a dichotomous variable. In addition i have constructed two (interaction) models, based on domain knowledge which use both variables to predict the ...
3
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0answers
93 views
Making new variable instead of correcting for temporal autocorrelation in a GLMM. Is it a valid alternative?
I am doing some forest disturbance research, in which the aim is to predict the probabilities of wind damage occurrence in forest stands of different site (altitude, slope steepness) and stand ...
1
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0answers
55 views
Interpreting odds ratios with log-transformed continuous variables in a logistic regression [duplicate]
Possible Duplicate:
Interpretation of log transformed predictors in logistic regression
Should quantitative predictors be transformed to be normally distributed?
I was hoping for some ...
3
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0answers
117 views
Can I include nominal predictor variables in ordinal regression?
I am in the process of performing ordinal regression on a large data set that measures different types of variables (nominal, ordinal, continuous). My outcome variable is ordinal, hence the ordinal ...
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3answers
756 views
Logistic regression performance with high number of predictors
I'm trying to understand the behavior of logistic regression in high dimensional problems (i.e. when you are fitting a logistic regression to data with a high number of predictor variables).
Every ...
3
votes
3answers
228 views
How to tell when factors “disagree” in linear regression to produce noisy predictions?
I use a regression as my predictor. Let's say my regression is $y = a_1 x_1 + a_2 x_2 + a_3 x_3$
I realized that in practice, when my prediction is way off, it's usually because one factor ...
3
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0answers
99 views
Using ordinal regression to evaluate predictor “importance”?
We've got a construct-likert-scale with an internal (8 items) and an external dimension (6 item) and there is also a 5-point item y assessing the "subjective" perception (How skilled do you think you ...
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1answer
75 views
Interpolating between models in ROC space
Suppose I have two models $A$ an $B$ that predict class labels. If these give binary predictions, these will appear as pairs of (false positive rate, true positive rate) in the ROC space. We should be ...
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4answers
2k views
What is the difference between estimation and prediction?
For example, I have historical loss data and I am calculating extreme quantiles (Value-at-Risk or Probable Maximum Loss). The results obtained is for estimating the loss or predicting them? Where can ...
5
votes
3answers
762 views
Should a predictor, significant on its own but not with other predictors, be included in an overall multinomial logistic regression?
I constructed a model via multinominal logistic regression analysis. The final model contains three predictors. All predictors are significant when they are the only predictors. However, the ...
6
votes
2answers
947 views
What methods to use for statistical prediction/forecast of trading data?
I’m working on a trading system and need to apply some statistics on the results. Unfortunately I forgot all about statistics after I left university over a decade ago and now I really have no clue ...
4
votes
1answer
121 views
How do I create a predictor for a time series once I've confirmed Granger-causality?
I have a set of time series data that I've found granger-causality (i.e. regressed Y vs. X, X-1, Y-1), and am wondering how I can create a predictor from this linear model? Is it simply the ...
9
votes
4answers
4k views
Maximum number of independent variables that can be entered into a multiple regression equation
What is the limit to the number of independent variables one may enter in a multiple regression equation? I have 10 predictors that I would like to examine in terms of their relative contribution to ...
3
votes
1answer
439 views
Should quantitative predictors be transformed to be normally distributed?
I am always struggling with normality testing for quantitative predictors (no factors) and transforming them to normality.
If I am running a GLMM and my predictors are really non-normal, should I ...
9
votes
4answers
895 views
Comparing importance of different sets of predictors
I was advising a research student with a particular problem, and I was keen to get the input of others on this site.
Context:
The researcher had three types of predictor variables. Each type ...
10
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2answers
2k views
When and how to use standardized explanatory variables in linear regression
I have 2 simple questions about linear regression:
When is it advised to standardize the explanatory variables?
Once estimation is carried out with standardized values, how can one predict with new ...
5
votes
2answers
243 views
Chi-square analog for context-dependent distributions
Lets imagine that we have some experiments. Each experiment may result in one of the outcomes: A, B, C. So we have probabilities distribution for each experiment $P_A, P_B, P_C$ which is ...
9
votes
3answers
4k views
Regression coefficients that flip sign after including other predictors
Imagine you,
Run a linear regression with four numeric predictors (IV1, ..., IV4)
When only IV1 is included as a predictor the standardised beta is +.20
When you ...
