Hadgu, Alula; (1993).Repeated Measures Data Analysis with Nonnormal Outcomes."
... individual changes over time. The distinction between marginal and transitional models will be discussed in the next chapters. General approaches for the analysis of repeated measures data are available for both continuous and categoric:al response ...
... individual changes over time. The distinction between marginal and transitional models will be discussed in the next chapters. General approaches for the analysis of repeated measures data are available for both continuous and categoric:al response ...
Scalable Density-Based Distributed Clustering
... global site to be analyzed centrally there. On the other hand, it is possible to analyze the data locally where it has been generated and stored. Aggregated information of this locally analyzed data can then be sent to a central site where the information of different local sites are combined and an ...
... global site to be analyzed centrally there. On the other hand, it is possible to analyze the data locally where it has been generated and stored. Aggregated information of this locally analyzed data can then be sent to a central site where the information of different local sites are combined and an ...
Mining Interval Time Series
... as being “active” for a period of time. For many applications, events are better treated as intervals rather than time points [5]. As an example, let us consider a database application, in which a data item is locked and then unlocked sometime later. Instead of treating the lock and unlock operation ...
... as being “active” for a period of time. For many applications, events are better treated as intervals rather than time points [5]. As an example, let us consider a database application, in which a data item is locked and then unlocked sometime later. Instead of treating the lock and unlock operation ...
Consensus Clustering
... combined clustering unattainable by any single clustering algorithm; are less sensitive to noise, outliers or sample variations; and are able to integrate solutions from multiple distributed sources of data or attributes. In addition to the benefits outlined above, consensus clustering can be useful ...
... combined clustering unattainable by any single clustering algorithm; are less sensitive to noise, outliers or sample variations; and are able to integrate solutions from multiple distributed sources of data or attributes. In addition to the benefits outlined above, consensus clustering can be useful ...
Subspace Clustering of High-Dimensional Data: An Evolutionary
... of dense regions it eliminates outliers. The discussion details key aspects of the proposed MOSCL algorithm including representation scheme, maximization fitness functions, and novel genetic operators. In thorough experiments on synthetic and real world data sets, we demonstrate that MOSCL for subsp ...
... of dense regions it eliminates outliers. The discussion details key aspects of the proposed MOSCL algorithm including representation scheme, maximization fitness functions, and novel genetic operators. In thorough experiments on synthetic and real world data sets, we demonstrate that MOSCL for subsp ...
BOAI: Fast alternating decision tree induction based on bottom-up evaluation
... cases. Suppose there are N instances at node p and the mapped value field on A is range from 0 to M − 1, where M is the number of distinct values of A. It takes one pass over N instances mapping their weights into the value field of A. Then the attribute values together with their corresponding weig ...
... cases. Suppose there are N instances at node p and the mapped value field on A is range from 0 to M − 1, where M is the number of distinct values of A. It takes one pass over N instances mapping their weights into the value field of A. Then the attribute values together with their corresponding weig ...
PDF - UZH - Department of Economics
... of the regression coefficients. Without loss of generality, assume that the explanatory variables are ordered in such a way that the coefficients of interest correspond to the first S coefficients, so θ = (θ1 , . . . , θS )0 . One typically is in the two-sided setup (2) where the prespecified value ...
... of the regression coefficients. Without loss of generality, assume that the explanatory variables are ordered in such a way that the coefficients of interest correspond to the first S coefficients, so θ = (θ1 , . . . , θS )0 . One typically is in the two-sided setup (2) where the prespecified value ...
Computability and Complexity Results for a Spatial Assertion
... The undecidability result in the previous section indicates that in order to obtain a decidable fragment of the assertion language, either quantifiers must be taken out in the fragment or they should be used in a restricted manner. In this section, we consider the quantifier-free fragment of the asser ...
... The undecidability result in the previous section indicates that in order to obtain a decidable fragment of the assertion language, either quantifiers must be taken out in the fragment or they should be used in a restricted manner. In this section, we consider the quantifier-free fragment of the asser ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.