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Regression dilution is the term which is applied when a covariate in the model cannot be measured directly and instead of that a related observed value must be used in analysis. In common, if the model is correctly specified in the terms of the 'true' covariate, then an equivalent form of the model with a easy error structure will not hold for observed values. In such type of cases, ignoring the measured values will lead to the biased estimates of the parameters in the model. It is often also referred to as the errors in variables problem.
The generalization of the normal distribution used for the characterization of functions. It is known as a Gaussian process because it has Gaussian distributed finite dimensional m
Length-biased data is a data which arise when the probability that an item is sampled is proportional to its own length. A main example of this situation occurs in the renewal the
The model which arises in the context of estimating the size of the closed population where individuals within the population could be identified only during some of the observatio
Conjugate prior : The distribution for samples from the particular probability distribution such that the posterior distribution at each stage of the sampling is of the identical f
Categorizing continuous variables : A practice which involves the conversion of the continuous variables into the series of the categories, which is common in the field of medical
Incidental parameter problem is a problem which sometimes occurs when the number of parameters increases in the tandem with the number of observations. For instance, models for pa
Multi-hit model is the model for a toxic response which results from the random occurrence of one or the more fundamental biological events. A response is supposed to be induced o
Multimodal distribution is the probability distribution or frequency distribution with number of modes. Multimodality is frequently taken as an indication which the observed di
Huffman code is used to compress data file, where the data is represented as a sequence of characters. Huffman's greedy algorithm uses a table giving how often each character occur
The Null Hypothesis - H0: There is no first order autocorrelation The Alternative Hypothesis - H1: There is first order autocorrelation Durbin-Watson statistic = 1.98307
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