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Logistic Regression
Logistic Regression

what is the value of the GED for dropouts who
what is the value of the GED for dropouts who

... differences and contains all of the control variables of the first model. However, this model adjusts for differences in earnings and employment dynamics prior to the GED attempt by including as regressors log earnings and employment indicators by quarter for quarters –7 through –1 before the GED at ...
An Introduction to Bootstrap Methods with Applications to R
An Introduction to Bootstrap Methods with Applications to R

... sampling distribution, the inferences are accurate. The accuracy improves as the size of the original sample increases, if the central limit theorem applies. “Resampling” as a concept was first used by R. A. Fisher (1935) in his famous randomization test, and by E. J. G. Pitman (1937, 1938), althoug ...
Three-dimensional organization of genomes: interpreting chromatin
Three-dimensional organization of genomes: interpreting chromatin

Testing Conditional Factor Models
Testing Conditional Factor Models

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5. Sample Size, Power & Thresholds

Removal of Orange 16 reactive dye from aqueous solutions by
Removal of Orange 16 reactive dye from aqueous solutions by

Diagnosing Harmful Collinearity in Moderated Regressions
Diagnosing Harmful Collinearity in Moderated Regressions

Report to Users
Report to Users

Non-orthogonal Designs
Non-orthogonal Designs

... ANOVA can be accomplished as t − tests. Because of the simplicity of the design, we can see some important points that generalize to 2-Way ANOVA’s with more than 2 levels per factor. Scott Maxwell, in Chapter 7 of his classic textbook Designing Experiments and Analyzing Data, gives the following int ...
Pivotal Estimation via Square-root Lasso in
Pivotal Estimation via Square-root Lasso in

Méthodes non-paramétriques pour la prévision d - ENAC
Méthodes non-paramétriques pour la prévision d - ENAC

... the reliability of an interval prediction model. We also introduce two measures for comparing different interval prediction models giving intervals that have different sizes and coverage. Starting from our work on statistical intervals (and the associated possibility distribution), we present a pair ...
Pivotal Estimation via Square-root Lasso in Nonparametric
Pivotal Estimation via Square-root Lasso in Nonparametric

... normality results of [8, 3] on a rather specific linear model to a generic nonlinear problem, which covers covers smooth frameworks in statistics and in econometrics, where the main parameters of interest are defined via nonlinear instrumental variable/moment conditions or z-conditions containing un ...
The CALIS Procedure
The CALIS Procedure

... squares estimation. Alternatively, model outliers can be downweighted during model estimation with robust methods. If the number of observations is sufficiently large, Browne’s asymptotically distribution-free (ADF) estimation method can be used. If your data sets contain random missing data, the fu ...
Report on: - Science Advisory Board for Contaminated Sites in
Report on: - Science Advisory Board for Contaminated Sites in

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An introduction to Bootstrap Methods Outline Monte Carlo

... Hypothesis testing via parametric bootstrap is also known as Monte Carlo tests. Alternative testing procedures are the so-called permutation or randomization tests. The idea is applicable when the null hypothesis implies that the data do not have any structure and thus, every permutation of the samp ...
ch. 4 maximum entropy distributions
ch. 4 maximum entropy distributions

Vector Autoregressions with Parsimoniously Time Varying
Vector Autoregressions with Parsimoniously Time Varying

... the literature on time varying parameter models, and then detail our contributions before turning to the specifics of our model and estimation method. There exists a substantial literature on time varying parameter models in every domain of time series econometrics. Using a Bayesian approach, Koop a ...
Vector Autoregressions with Parsimoniously Time
Vector Autoregressions with Parsimoniously Time

... the literature on time varying parameter models, and then detail our contributions before turning to the specifics of our model and estimation method. There exists a substantial literature on time varying parameter models in every domain of time series econometrics. Using a Bayesian approach, Koop a ...
Likelihood-ratio-based confidence sets for the timing of structural
Likelihood-ratio-based confidence sets for the timing of structural

... the Great Moderation was an abrupt change in the mid-1980s rather than a gradual reduction in volatility, potentially providing insight into its possible sources (see Morley (2009)). Meanwhile, when taking co-integration between output and consumption into account, confidence sets for structural bre ...
1.14 Polynomial regression
1.14 Polynomial regression

... thus the mean is a d’th order polynomial in the covariate y. Let y1 , . . . , yn be given, real numbers – the covariates – and Xi = β 0 + β 1 y + β 2 y 2 + . . . + β d y d + ε i where the εi ’s are iid with the N (0, σ 2 )-distribution. Then we can estimate the d + 1 parameters β0 , . . . , βd by le ...
2416grading2415 - Emerson Statistics
2416grading2415 - Emerson Statistics

... significant with a significance level of 0.05 (P<0.001). The increase will not be unusual if the true geometric mean of increase amount falls within the interval of 0.0122mg/dl to 0.0157 mg/dl, or the mean of fib on original scale will increase to 1.0122 to 1.0158 times as crp level goes up per unit ...
Robust Confidence Intervals in Nonlinear Regression under Weak Identification
Robust Confidence Intervals in Nonlinear Regression under Weak Identification

Assessing statistical significance in multivariable
Assessing statistical significance in multivariable

When Everyone Misses on the Same Side: Debiased Earnings Surprises and Stock Returns
When Everyone Misses on the Same Side: Debiased Earnings Surprises and Stock Returns

1 2 3 4 5 ... 178 >

Data assimilation

Data assimilation is the process by which observations are incorporated into a computer model of a real system. Applications of data assimilation arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. The most commonly used form of data assimilation proceeds by analysis cycles. In each analysis cycle, observations of the current (and possibly past) state of a system are combined with the results from a numerical model (the forecast) to produce an analysis, which is considered as 'the best' estimate of the current state of the system. This is called the analysis step. Essentially, the analysis step tries to balance the uncertainty in the data and in the forecast. The result may be the best estimate of the physical system, but it may not the best estimate of the model's incomplete representation of that system, so some filtering may be required. The model is then advanced in time and its result becomes the forecast in the next analysis cycle. As an alternative to analysis cycles, data assimilation can proceed by some sort of nudging process, where the model equations themselves are modified to add terms that continuously push the model towards observations.
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