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example, then P(X (2) < 1645) = 0, because by de nition, X (2) X (1) That is, X (1) and X (2) are dependent More generally, if independent observations are put in order and some extreme values are removed, the remaining observations are no longer independent When sampling from a normal distribution, or from any light-tailed distribution, methods based on medians can have substantially less power than do methods based on means Is there some alternative measure of location that performs about as well as the mean under normality but that guards against low power when sampling from a heavy-tailed distribution There are in fact three such measures of location that seem to have considerable practical value: trimmed means, M-estimators, and a modi ed one-step M-estimator called MOM.



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Among the reasons given for the deletion were the dif culty in handling the plug-in , printing restrictions and that the state-of-the-art in GSM handsets shows that it is no more a problem to integrate an ID-1 card in the handset as some mobile manufacturers provides now a full ID-1 card interface (as) part of the original design To follow the request would have been a dealignment with GSM, also affecting the planned DECT-GSM interworking..





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Not only do they enhance power, but also excellent methods for controlling the probability of a Type I error have been devised that continue to perform well in situations where methods based on means are highly unsatisfactory Trimmed Means Trimmed means are characterized by trimming a xed (predetermined) proportion of observations Typically, the same amount of trimming is done from both tails That is, a trimmed mean removes a speci ed proportion of the largest observations and repeats this for the smallest observations; then the remaining observations are averaged Note that the mean and median represent two extremes: no trimming and the maximum amount of trimming that can be done Trimming 20% from both tails maintains relatively high power under normality, but power remains fairly high when sampling, for example, from the mixed normal.

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As previously mentioned, removing extreme values creates a technical problem: The remaining observations are dependent, so there is the practical issue of how to estimate its standard error Tukey and McLaughlin (1963) were the rst to deal with this issue A description of their method is given in the next section Another strategy is to check empirically for outliers, remove any that are found, and average the values that remain This includes the class of skipped estimators that was originally suggested by Tukey Recently, the particular variation of this method called MOM has been found to be especially useful (Wilcox, in press) In the past, technical problems precluded the routine use of these estimators when testing hypotheses, but recent advances make them a viable option To explain part of the motivation behind MOM requires some preliminary remarks about detecting outliers.

A modeling concept developed by Barash [46] and Heitsch et al [47] who noted that the essential arrangement of loops and stems in RNA secondary structure is captured by a tree if one excludes the pseudoknots A pseudoknot can be conceptualized as switchbacks in the folding of secondary structure With the exclusion of pseudoknots, the geometric skeleton of secondary RNA structure is easily visualized as a tree as in Figure 37 Unlike the classic model developed by Waterman et al where atoms are represented by vertices and bonds between the atoms by edges in the graph, this model represents stems as edges and breaks in the stems that result in bulges and loops as vertices A nucleotide bulge, hairpin loop, or internal loop are each represented by a vertex when there is more than one unmatched nucleotide or noncomplementary base pair.

There are some well known and fairly obvious ways of detecting outliers based on the mean and variance A commonly used.

endorsed the PsyD degree as an alternative to the PhD degree. 6. In 1977, George Engel offered the biopsychosocial approach as the best possible model of understanding and treating physical and mental illness. The approach suggests that all physical and psychological illnesses and problems are likely to have a biological, psychological, or social element, which should be understood in order to provide the most effective intervention strategies. The biopsychosocial model has been widely accepted in both medicine and psychology with strong research support to demonstrate its effectiveness. The biopsychosocial perspective has quickly become an influential model in clinical psychology.

Researchers at New York University in the Computational Biology Group led by Tamar Schlick used this method to create an RNA topology database called RAG (RNA As Graphs) that is published and available at BMC Bioinformatics and Bioinformatics [48,49] The RNA motifs in RAG are cataloged by their vertex number and Fiedler eigenvalues This graph theoretic representation provides an alternative approach for classifying all possible RNA structures based on their topological properties In this work, Schlick et al nd that existing RNA classes represent only a small subset of possible 2D RNA motifs [50,51] This indicates that there may be a number of additional naturally occuring secondary structures that have not yet been identi ed It also points to possible structures that may be utilized in the synthesis of RNA in the laboratory for drug design purposes.

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