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MCAR, MAR, MNAR, multiple imputation
MCAR, MAR, MNAR, multiple imputation

... (a) Impute observations for all missing values in a variable V : use random samples from normal distribution with mean and SD of V . (Or use regression to predict mean and SD, then sample from this normal distribution.) (b) Do the imputation M times, creating M complete data sets. (c) Analyze each o ...
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... The students have learned a great deal about weather forecasting. They have read a number of books, visited websites, watched the Weather Channel, and interviewed a local meteorologist. Jalica Brown, a sixth grader commented, “It’s so great to be able to learn something in school that is about real ...
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Basics of Data Analysis

... – Has a probability of alpha (α). – Practically, a Type I error occurs when the researcher concludes that a relationship or difference exists in the population when in reality it does ...
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On the Use of Data-Mining Techniques in Knowledge

... some previously given hypotheses, while human intuition helps the discovery guiding so that it gathers the information wanted by the user, in a certain time window. Data mining could be applied to any domain where large databases are saved. Examples of DM applications: prediction problems such as th ...
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Chapter 11 - Monté Carlo Simulation

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Elliott

... “The idea of imputation is both seductive and dangerous. It is seductive because it can lull the user into the pleasurable state of believing that the data are complete after all, and it is dangerous because it lumps together situations where the problem is sufficiently minor that it can be legitima ...
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REGRESSION WITH TIME SERIES VARIABLES

... • Minimal changes (e.g. OLS fine, testing done in standard way, etc.), except for the interpretation of results. • Lag lengths, p and q can be selected using sequential tests. • It is convenient to rewrite ADL model as: ...
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REGRESSION WITH TIME SERIES VARIABLES
REGRESSION WITH TIME SERIES VARIABLES

... • Minimal changes (e.g. OLS fine, testing done in standard way, etc.), except for the interpretation of results. • Lag lengths, p and q can be selected using sequential tests. • It is convenient to rewrite ADL model as: ...
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... where () is the distribution function of the standard normal distribution. Estimates of [,, ] were obtained by using “an iterated, seemingly unrelated Marquardt-Levenberg technique” (Burnett, 1977, 372). Note the use of the ‘residual’ in the second equation to mitigate the simultaneity of the ...
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... Covalent forces acting on the atom Neighbors that must be considered in non-bonded forces Non-bonded forces Update position and velocity Misc physical properties based on motions ...
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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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