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Wald Test While the LM test asks which parameters, if any, should be added to a model, the Wald test asks which, if any, could be deleted Are there any parameters that are currently being estimated that could, instead, be xed to zero Or, equivalently, which parameters are not necessary in the model The Wald test is analogous to backward deletion of variables in stepwise regression, where one seeks a nonsigni cant change in R 2 when variables are left out When the Wald test is applied to the example, the only candidate for deletion is the regression coef cient predicting Acceptance of Risky Behavior (ACCEPT_RISK) from gender If this parameter is dropped, the chi-square value increases by 1134, a nonsigni cant change ( p = 287) The model is not signi cantly degraded by deletion of this parameter.

1/ [(10 6 / 95.5) + (10 6 / 166)] 1/ (0.016 10 6 )

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In this section, we summarize the trade-offs between event-driven run-tocompletion sensor operating systems such as TinyOS, and preemptively timesliced multithreaded sensor operating systems such as MANTIS OS. Table 6.1 compares the advantages and disadvantages of each approach. An event-driven run-to-completion operating system is well suited to highly memory-constrained devices such as the original Rene motes, which had only 512 bytes of SRAM [1]. Indeed, it is unlikely that a multithreaded system would be capable of being implemented in such limited memory while also being able to support multiple threads of execution. As a result, event-driven designs represent the primary option, given severe memory constraints. An event-driven system also offers the advantage of being well-suited to achieve energy ef ciency. When there are no events to be handled, for example, sensing events or radio packet events, then the system need not execute, and can sleep itself. As a result, eventdriven systems were perfectly adapted to the rst generation of wireless sensor nodes. The drawbacks of an event-driven system relate to its run-to-completion paradigm. Each task must run to completion before another task can execute. As a

The decision is made to keep the path because this path was central to the hypothesis However, if the goal is development of a parsimonious model, it might also be reasonable to drop the path Notice that unlike the LM test, nonsigni cance is desired when using the Wald test When only a single parameter is evaluated, the Wald test is just the square of the z test given previously (1134 = 10652 ) Some Caveats and Hints on Model Modi cation Because both the LM test and Wald test are stepwise procedures, Type I error rates are in ated with the exception of the Scheffe-like procedure suggested by Hancock (1999) There are, as yet, no available adjustments as in analysis of variance (ANOVA) A simple approach is to use a conservative probability value (say, p < 01) for adding parameters with the LM test.

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Cross validation with another sample is also highly recommended if modi cations are made If numerous modi cations are made and new data are not available for cross validation, compute the correlation between the estimated parameters from the original, hypothesized model and the estimated parameters from the nal model using only parameters common to both models If this correlation is high (> 90), relationships within the model have been retained despite the modi cations Unfortunately, the order that parameters are freed or estimated can affect the signi cance of the remaining parameters MacCallum (1986) suggested adding all necessary parameters before deleting unnecessary parameters In other words, do the LM test before the Wald test A more subtle limitation is that tests leading to model modi cation examine overall changes in chi-square, not changes in individual parameter estimates Large changes in chi-square are sometimes associated with very small changes.

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