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Ignorability: The missing data mechanism is said to be ignorable for likelihood inference if (1) the joint likelihood for the responses of the interest and missing data indicators can be decomposed into the two separate components (containing parameters of the main interest and the parameters of the missingness mechanism,) and (2) the parameters for each component are distinct in the sense that there are no parameter restrictions across components. The component for the missingness mechanism can then be unnoticed in statistical inference for the parameters of interest. Ignorability follows if the missing values are missing completely at random or missing at random and the parameters are distinct.
The approach to data analysis which emphasizes the use of informal graphical procedures not based on former assumptions about structure of the data or on the formal models for the
Particlefilters is a simulation method for tracking moving target distributions and for reducing computational burden of the dynamic Bayesian analysis. The method uses a Markov ch
Formal graphical representation of the "causal diagrams" or the "path diagrams" where the relationships are directed but acyclic (that is no feedback relations allowed). Plays an
Multimodal distribution is the probability distribution or frequency distribution with number of modes. Multimodality is frequently taken as an indication which the observed di
calculate absorbance value from concentration
The division of a sample of observations into several classes, together with the number of observations in each of them. It acts as a useful summary of the main features of the da
The type of longitudinal study in which the subjects receive different treatments on the various occasions. Random allocation is required to determine the order in which the treatm
MEANING ,IMPORTANCE AND RELEAVANCE OF SCATTER DIAGRAM
Yate s' continuity correction : When the testing for independence in contingency table, a continuous probability distribution, known as chi-squared distribution, is used as the app
Median absolute deviation (MAD) : It is the very robust estimator of the scale given by the following equation or, in other words we can say that, the median of the absolute
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