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Like RIP, IGRP is a classful, distance vector routing protocol. It operates in a very similar way to RIP, periodically broadcasting its entire routing table via all interfaces. It also requests information from all neighbours when it starts up to obtain a complete routing table as quickly as possible. IGRP uses the concept of autonomous systems as process identi ers. IGRP autonomous systems do not necessarily correspond to the concept of autonomous systems in OSPF or BGP. In IGRP an autonomous system represents a set of routers using a common IGRP routing process to exchange information. It is possible, within a single IGP, to de ne multiple IGRP instances between which it is possible to tightly control the exchange of routing information. Unlike other routing protocols, IGRP uses a composite metric system that allows it to overcome, to a certain extent, the constraints of RIP. IGRP still uses the hop count to a destination as an element of the metric and can be used in networks up to 255 hops in diameter. Composite metrics are derived from four link characteristics: bandwidth, delay, load and reliability. By default, IGRP uses just the bandwidth and delay, only using load and reliability if speci cally con gured to do so. Bandwidth and delay are static values not directly linked to the actual bandwidth and delay of the associated link. By default, each link type has a particular value. However, these values can be manually con gured. Bandwidth is transmitted as a three octet number derived from the bandwidth in kilobits per second scaled by a factor of 107 . Delay is also used to represent unreachability. Delay is measured in tens of milliseconds and transmitted as a three octet number. An unreachable destination has a delay of 0xFFFFFE

+2 =2

January return: February return: Total period return:

<A> (2) (3)

This factor is completely different from the previous one (backup/recovery) in that it refers to the general condition of systems in combination with the overall level of the computer systems ef ciency. For example, two rms with identical systems can receive different scores if one of the rms uses the systems more ef ciently. When conducting an on-site visit, take a tour of the of ce space and make note of the age and wear of the systems. When sitting with investment professionals in their of ces (or at their desks), ask for a tour of their internal systems. Score/Methodology 0 Computer systems are grossly inadequate for the rm s needs. 1 Computer systems are less than adequate for the rm s needs and may be interfering with the rm s overall ef ciency and/or the ef ciency of the investment staff. Firm needs to upgrade hardware/software now.

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While combining the multiple estimates is relatively straightforward, constructing the multiply imputed datasets is difficult Again, there is a parametric and a nonparametric approach In the parametric approach, the multiply imputed (MI) datasets are simulated draws from a Bayesian predictive distribution of the missing data This requires a model for the complete data, and properly adding uncertainty about both the missing values and the parameters of the predictive distribution Schafer (1996) describes such procedures for normally distributed data and for other data models, including categorical and mixed normal categorical data and panel data In the nonparametric approach hot deck imputation is used The approach is to use a bootstrap procedure to generate the random imputations First, a bootstrapped logistic regression is used to predict the nonresponse Next, the regression equation is used to compute the propensity to have missing data for each case in the sample.

A natural question is: Which are the polynomials Q(i) such that the function on the right-hand side of (12.17) is a covariance (i.e., nonnegative de nite) A simple way to construct a class of covariances of this type is to take Q(i) (u, v, w) = P (u, vw), (12.19)