Chapter 15
Chapter 15

... data, because ut is not i.i.d. – ut can be serially correlated!  This means that the usual OLS standard errors (usual STATA printout) are wrong!  We need to use, instead, SEs that are robust to autocorrelation as well as to heteroskedasticity… ...
Slides - Gary Holness
Slides - Gary Holness



... that represents the slope of the model. It represents the change in the logit of the outcome variable (natural logarithm of the odds of Y occurring) associated with a one-unit change in the predictor variable ...
final05
final05

Field-dependence of relaxation time distributions in rock samples V
Field-dependence of relaxation time distributions in rock samples V

Change Detection in Multivariate Datastreams: Likelihood
Change Detection in Multivariate Datastreams: Likelihood

... consists in computing the log-likelihood of the datastream and compare the distribution of the log-likelihood over different time windows (Section 2). In practice, computing the log-likelihood is an effective way to reduce the multivariate change-detection problem to a univariate one, thus easily ad ...
Field-dependence of relaxation time distributions in rock samples V
Field-dependence of relaxation time distributions in rock samples V

Making the Most of Statistical Analyses: Improving Interpretation and Presentation
Making the Most of Statistical Analyses: Improving Interpretation and Presentation

A topography-based model of forest cover at the
A topography-based model of forest cover at the

The UK Independence Party (UKIP), a populist right party, came first
The UK Independence Party (UKIP), a populist right party, came first

Learning Sparse Gaussian Bayesian Network Structure by Variable
Learning Sparse Gaussian Bayesian Network Structure by Variable

Is There a Stable Relationship between Unemployment and Future
Is There a Stable Relationship between Unemployment and Future

document
document

Survey Algebra - Lakewood City School District
Survey Algebra - Lakewood City School District

A Note on Nonparametric Identification of Distributions
A Note on Nonparametric Identification of Distributions

department of defense weather programs
department of defense weather programs

... clouds, ground, snow, and water. It uses both stellar and inertial references, together with on-board processors, to maintain stability and pointing accuracy. The DMSP also flies a microwave temperature and humidity sounder (SSM/T, SSM/T-2) which provides vertical temperature, moisture, and height p ...
Applying Flexible Parameter Restrictions in Markov
Applying Flexible Parameter Restrictions in Markov

Regress Lecture 1
Regress Lecture 1

The *Art* of Municipal Revenue Forecasting in Arizona
The *Art* of Municipal Revenue Forecasting in Arizona

... – One-Time Revenue (Including Fund Balance) • Not Used for Recurring Expenditures ...
Modeling Rainfall Prediction using Fuzzy Logic
Modeling Rainfall Prediction using Fuzzy Logic

Application of loglinear models to claims triangle
Application of loglinear models to claims triangle

Logistic Regression - Virgil Zeigler-Hill
Logistic Regression - Virgil Zeigler-Hill

Classification with correlated features
Classification with correlated features

Finding a Meaningful Model
Finding a Meaningful Model

... between your variables are linear. If they aren’t, you can try to transform your variables so that the relationships become linear. A histogram is another useful output from a scatterplot. Create one for each variable. If some explanatory variables are strongly skewed, you may be able to remove mo ...
Chapter 4 Loads on marine structures
Chapter 4 Loads on marine structures

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.