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Comparing exemplar and prototype models of categorization Canadian Journal of Experimental Psychology, 51, 212 223 Douglas, K S (2000, March) A psycholegal analysis of violence risk assessment: Bringing law, science, and practice closer together Paper presented at the 2000 Biennial Meeting of the American Psychology-Law Society (Div 41 APA), New Orleans, LA Douglas, K S (2001) Making structured clinical decisions about violence risk: The reliability and validity of the HCR-20 violence risk assessment scheme Unpublished manuscript Douglas, K S, Cox, D N, & Webster, C D (1999) Violence risk assessment: Science and practice Legal and Criminological Psychology, 4, 149 184 Douglas, K S, Huss, M T, Murdoch, L L, Washington, D O, & Koch, W J (1999) Posttraumatic stress disorder stemming from motor vehicle accidents: Legal issues in Canada and the United States In E J Hickling & E B Blanchard (Eds.

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Estimation This step produces location estimation of the measured object using k closest entries selected in the matching step. If k is equal to 1, the selected entry is the closest entry and considered to be the location of the object. On the other hand, if k is larger than 1, the estimation step then uses different algorithms to derive the estimated location from these k entries. The rationale is that often there are multiple neighbors that are roughly the same distance from the measured object. Given the large variation of RF measurements, there is no fundamental reason to pick up only the closest neighbor and reject others that are almost close. It has already been shown [8,10] that algorithms such as the k nearest-neighbor averaging and the smallest k-vertex polygon can provide more accurate estimation. The k nearest-neighbor average requires k closed locations be returned and then uses the average of these k locations as the location estimations. Smallest k-vertex polygon nds k locations that form a polygon with smallest perimeters and then take the average of these k locations as the location estimation. As shown in Figure 13.7, when k is equal to 3, three (P3, P4, and P5) among total seven points

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), International handbook of road traf c accidents and psychological trauma: Current understanding, treatment and law (pp 271 290) New York: Elsevier Science Douglas, K S, & Kropp, P R (in press) A prevention-based paradigm for violence risk assessment: Clinical and research applications Criminal Justice and Behavior Douglas, K S, Ogloff, J R P, Nicholls, T L, & Grant, I (1999) Assessing risk for violence among psychiatric patients: The HCR-20 violence risk assessment scheme and the Psychopathy.

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This section focuses on the components that should be included when deciding to adopt a PMS. The decision-making process begins with understanding the importance of a needs analysis performed by a team of frontline staff members. The needs analysis should focus on the ow of the guests through the hotel and interdepartmental communication needs. A review of administrative paperwork produced by management in all areas of the hotel is also a consideration. After management has gathered relevant data concerning the operational needs, it must objectively determine whether a computer will help to improve guest service. Other important concepts covered here include software selection considerations and computer hardware terminology. A review of how people interact with computers and how a hotel must make provisions while hardware is being installed is also offered. The importance of computer training and planning a backup power source for continued computer operation is reviewed. The often overlooked maintenance agreement and the very important nancial payback complete the discussion of selecting a PMS.

latent transition models In order to assess the statistical power of any test involving the G2, the distribution of the G2 under the alternative hypothesis, called the noncentral distribution, is needed As the central distribution of G2 is unknown when the contingency table is sparse, it follows that the noncentral distribution is unknown as well Of course, the usual factors that affect statistical power operate in this context namely, sample size, choice of alpha, and effect size which in model selection is the overall difference between the true model and the model under consideration It is possible that the Bayesian posterior predictive check distribution approach to hypothesis testing discussed above will provide an avenue for assessing statistical power for latent class models Sample Size and Related Considerations In addition to statistical power considerations, other sample size considerations come up in LCA and LTA.

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