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Zero-inflated Poisson regression is the model for count data with the excess zeros. It supposes that with probability p the only possible observation is 0 and with the probability 1 p a random variable with the Poisson distribution is observed. For instance, when manufacturing equipment is properly aligned, defects might be almost impossible. But when it is misaligned, defects might happen according to a Poisson distribution. Both probability p of the perfect zero defect state and the mean number of defects λ in the imperfect state might depend on covariates. The parameters in this type of models can be estimated using maximum likelihood estimation.
Quasi-experiment is a term taken in use for studies which resemble experiments but are weak on some of the characteristics, particularly that allocation of the subjects to groups
Oracle property is a name given to techniques for estimating the regression parameters in the models fitted to high-dimensional data which have the property that they can correctl
Initial data analysis (IDA): The first phase in the examination of the data set which comprises number of informal steps including the following steps * checking the quality o
The distribution over distributions in the sense that each draw from the process is itself the distribution. The name Dirichlet process or procedure is due to the fact that the ?ni
Probabilistic matching is a method developed to maximize the accuracy of the linkage decisions based on the level of agreement and disagreement among the identifiers on different
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The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2 >
Percentile : The set or group of divisions which produce exactly 100 equal parts in the series of continuous values, like blood pressure, height, weight, etc. Hence a person with b
Generalized principal components analysis: The non-linear version of the principal components analysis in which the goal is to determine the non-linear coordinate system which is
hello I have a dataset including both categorical & numerical variable for market segmentation.how can i cluster them via k-means in matlab? thank you
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