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We know intuitively that high-risk ventures should yield a risk premium and thus higher returns. In 1 we even quanti ed risk in terms of a standard deviation s of a probability distribution of returns. What we want to do in this chapter is to go one step further to answer the question How much more should I get for taking additional risk We will answer this question according to traditional methods, and point to places that these methods can be improved or, in some cases, where these methods have already been improved but the improvement has not been widely implemented. Standard deviation is a valuable concept for modeling volatility of stock returns, but the estimated standard deviations often seem foreign to an investor and not easily transferred to the bottom line: the price. Since business managers and bankers think of risk in terms of dollars of loss, standard deviation does not t very well into the language of business. It is not necessarily true that the higher the risk as measured by s, the higher are the losses. The senior management needs a number that gives the worst-case scenario of dollar loss, in a prescribed holding period. Enter value at risk (VaR). VaR puts a dollar amount on the highest expected loss on an investment. VaR has become popular since the US nancial giant J.P. Morgan published RiskMetricsTM methods in 1995. We will provide an overview of VaR in this chapter and many extensions that have been developed. For further details see surveys of VaR by Duffe and Pan (1997) and Jorion (1997). Some issues involving the time horizon for VaR need
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Challenges include the lack of a global Network Context; the need to minimize overhead required for coordination across nodes; limited decision times; possible lossy communications; and so on. To address these challenges, an anytime coordination approach can be used that builds upon three recent advances: (i) factored MDPs, a unique approach for solving large-scale MDPs that exploits problem structure; (ii) coordination graphs, a distributed coordination algorithm that allows multiple nodes to ef ciently nd the optimal action setting through neighborhood communication; and (iii) distributed inference for coordinated decision-making. The impact of the communication required for coordination on the available resources is also incorporated. This effect can be evaluated by combining the models with the predictive approach developed for the Network Context Manager. Using estimates, the level of coordination can be traded against the bandwidth required to achieve this coordination. Some of these ideas have been put to test in a sensor network environment. Robustness to failures is a key issue in sensor networks. To address this issue, Paskin et al. [2005] describes a robust distributed inference architecture that addresses a wide class of important inference tasks. The distributed nature of the approach provides formal guarantees in terms of robustness to unreliable communication and sensor failures. Planning and control techniques using factored MDPs have been used with great success in sensor networks that control actuators and have local parameters that can be optimized. Objective Function. Broadly speaking, the goal of the objective function is to enable the COF to estimate the goodness of proposed control actions, in order to select the best offered action. This goodness is measured in terms of the effect of the action on the perceived utility of the resulting network from the point of view of a user, or application. A user-centric view of the network utility is adopted here; network-wide concerns (e.g., maintain low communication pro le, conserve power) are represented as constraints on resource availability rather than part of the utility itself. The network utility is a function of two factors: (1) application utility, given by acceptable latency, loss, jitter, out-of-orderliness speci cations and an unhappiness function that assigns a utility value to a particular point in the space of QoS parameters; and (2) mission priority, given by a function of the mission situation that assigns relative weights to particular applications at a particular point in time, independently of their type. Application utilities can be combined to compute the network utility, given mission priorities. The network utility model is based on a weighted sum of such individual utilities. The goal of COF is to minimize the sum, over the course of the mission, for every application that had any traf c and every measurement period, of average mission priority-weighted application unhappiness values in the measurement period. In computing the supervisory control actions, node COFs collectively optimize such a multiattribute objective function. Decisions are based on maximum expected utility computations. The proposed form of network utility allows for easy accommodation of new application
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Differentiated Attributes. Innovative, unique attributes that only your product has. These are the attributes that will command premium price and will cause a customer to ignore cheaper alternatives and pay for your products. When the Nike Air technology was introduced, it commanded premium price because it was unique in the market and captured customers imagination (and, therefore, their wallets). For a certain period, it was a differentiating attribute. When the technology was copied by competitors, the attribute dropped to the undifferentiated level. Availability of similar attributes from competitors usually results in price pressures and commoditization. Above and Beyond Attributes. Attributes that make your customers recommend your products to other people. At this stage, your customer moves from the private domain of paying a premium price for your products to the public domain of becoming an evangelist. These attributes give you a
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and over again for each new year. This saves a lot of work; once the le is created, it only needs to be edited and updated each year. Second, it does not take up precious storage space in the department. However, there is one big disadvantage to this method. One still needs time during the department s busiest rush period to physically create the new folders. 2. Create the actual physical le folders and store them until needed at the beginning of the new year. The disadvantage here is having to store the le folders until they are needed. Whichever method the payroll department chooses, it is important to make sure that the les are ready for the new year so that the department can keep running ef ciently. (e) Updating Procedure Manuals Writing procedure manuals is a huge undertaking that each payroll department must complete. And once the manuals are in place and are being utilized by the payroll staff, they are a tremendous help in maintaining an ef cient department. But what good does it do to take the valuable time to create rstrate procedure manuals if they are allowed to become obsolete because no one takes the time to keep them updated A review and/or update of the procedure manuals should be done annually to ensure total accuracy. The beginning of the year is an excellent time to begin this process. Once again, the question arises as to timing. Does the payroll department have to do this task at such a hectic time Not necessarily, or at least, not entirely. Procedures, such as those discussed back in Section 2.1, should be constantly updated as a process is changed or adjusted. The review at the beginning of the year should be reserved for items such as forms that have been updated or changed for the new year and new rates or wage bases that may be referenced in the manual. The full review can be done later in the year, when time is available.
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The director of the Teen Center might look to a state agency for help or seek out the director of a larger nonprofit that includes technicians on its staff. Spend some time with other experienced administrators going over their job descriptions for technical staff, but merely copying someone else s job description is not enough. It is important to know what really lies behind the impressive sounding phrases.What does the technician really do Which are the more time consuming tasks Which ones require the most skill
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To log into Google Analytics, you need a Google Account (a username and password) and an active Google Analytics Account, as 5 explains. You use a Google Account to access all Google s services such as Gmail and Google Calendar. Your Google Analytics Account is tied to the speci c Web sites you re tracking.
Development of short-circuit current peak values, (see Table 4-6)
where m is the complex re ection coef cient at the module and is the one-way phase delay through the cable. Note that Zin (0) = Zm , in agreement with Eq. (23). Then, obtaining the original re ection coef cient from Eq. (13), we have (Pozar, 2001, p. 35) Zm R0 j 2 e Zm + R0 R Zin ( ) = Zm R0 j 2 0 1 e Zm + R0 1+ = = (Zm + R0 )e+j + (Zm R0 )e j R0 (Zm + R0 )e+j (Zm R0 )e j
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