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- What is the relation between estimator and estimate?
In Lehmann's formulation, almost any formula can be an estimator of almost any property There is no inherent mathematical link between an estimator and an estimand However, we can assess--in advance--the chance that an estimator will be reasonably close to the quantity it is intended to estimate
- Estimator for a binomial distribution - Cross Validated
How do we define an estimator for data coming from a binomial distribution? For bernoulli I can think of an estimator estimating a parameter p, but for binomial I can't see what parameters to estim
- ML vs WLSMV: which is better for categorical data and why?
I was wondering which is a better estimator to use for categorical data: ML or WLSMV I saw on a discussion on the Mplus website that they recommend WLSMV for categorical data but didn't explain why
- r - Lavaan Estimator - Cross Validated
I am using Lavaan, and my data is non-normal (right skewed) and small (90 obs for each time) Should I use MLR or MLM estimator? I ask this because I am getting much better results with MLM, although I think MLR is more used I have tried testing the configural model with several variables of my model, one at the time, and I always get crappy RMSEA values, even if I get good CFI, TLI or RSMR
- What is the difference between a consistent estimator and an unbiased . . .
An estimator is unbiased if, on average, it hits the true parameter value That is, the mean of the sampling distribution of the estimator is equal to the true parameter value
- Unbiased estimators of skewness and kurtosis - Cross Validated
skewness unbiased-estimator kurtosis Cite Improve this question edited Jun 21, 2015 at 4:19
- Notation in statistics (parameter estimator estimate)
In statistics, it is very important to differentiate between the following three concepts which are often confused and mixed by students Usually, books denote by $\\theta$ an unknown parameter Th
- Why do we prefer unbiased estimators instead of minimizing MSE?
We do often seek the unbiased estimator with the smallest variability But sometimes convenience (or habit) leads to use of estimators that are not "optimal" according to some criterion
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