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a patient toleration level for a drug increases with effectiveness of treatment A few researchers in information systems have shown that a rm s nancial performance increases with an increase in its information technology (IT) investment Several models in economics, operations research, and transportation science are required to satisfy the monotonicity property The monotonicity roperty of the forecasting function makes it easy to aggregate forecasts For example, assuming monotonicity of individual preferences relates individual preferences to predict market behavior and preserving monotonicity reduces over tting problem in a nonlinear algorithm For a linear forecasting function, it is easy to check if the function satis es the monotonicity property Perhaps some measures such as positive values of regression coef cients can be used to ensure that the learned forecasting function satis es the monotonicity property When it is desired to use a nonlinear forecasting function, monotonicity can be preserved by (1) redesigning the learning algorithm so that the resulting forecasting function learned by the nonlinear algorithm preserves monotonicity, or (2) using the training data set that does not violate the monotonicity property There are several nonlinear machine learning forecasting algorithms available The examples of nonlinear machine learning algorithms include arti cial neural network (ANN), classi cation and regression tree (CART), and genetic programming (GP) All of these algorithms do not assume any particular functional form of the forecasting function and learn the nonlinear forecasting function based solely on the properties of the training data set Since the functional form of the machine learning algorithm depends on the training data, it may be necessary that the training data satisfy the monotonicity property When training data violate the monotonicity property, some data screening may be required to create a training data subsample that does not violate monotonicity In a small-size data set, such data screening may not be feasible due to very small resulting subsample size However, in mid- to large-size data sets, it may be possible to create a subsample that preserves monotonicity In mid-size data sets, if a subsample that preserves monotonicity is too small, then perhaps a large subsample, not containing all the original training data, may be created with the subsample data that weakly preserves monotonicity The term weak monotonicity means that most of the cases in the training subsample satisfy the monotonicity property, and a few cases may somewhat violate monotonicity The design of the data screening algorithm that may be used to create a subsample is an important issue It is important that the screening algorithm should be nonlinear, preserve monotonicity, and not assume any particular nonlinear functional form For multivariate input and one output, the data envelopment analysis (DEA) model seems to satisfy all the requirements The DEA model is a nonlinear (piecewise linear) and nonparametric model that is used to measure ef ciency of production units In this case, we use DEA as a data screening approach to create a subsample training data set that is weakly monotonic We use the ANN as a nonlinear forecasting model The contribution of our.

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Be kind to your tenants: That s one of the best ways to ensure they stay with you for as long as possible We discuss landlord tenant relations in s 13 and 14, but we would be remiss here if we didn t mention fostering good tenant relations as a means of weathering a tough economy To get some perspective, consider the opposite scenario how tenant turnover diminishes property value: Greater vacancies, less income: Sure, a tenant who gives notice and leaves may gift you with her deposit, but how quickly are you going to be able to rent out the unit she s vacated, especially if the rental market goes soft A good unit may rent out quickly, but you may face delays depending on market conditions, the time of year, and other factors Moreover, the less income you have to show for it, the less valuable your investment is as an income-producing property Potential purchasers aren t the only ones who want to know your property s income and performance Lenders perceptions are also influenced by a poor performance, and affect how much they re willing to give you to finance other projects Tenant replacement costs: As with any business, income isn t the only measure of performance Margins are lso an important indicator of how good an investment your business is Tenant turnover not only threatens income, but also narrows your margins by forcing you to advertise and spend on other expenses you wouldn t have faced had the tenant stayed Word of mouth: Just as positive tenant experiences can be good advertising for you, bad experiences may get around and reduce demand for your property Disgruntled tenants warn friends not to rent from you, and you may end up with only the most desperate or unaware tenants not the kind you want, because they put your investment at greater risk of damage Bad will: Just as goodwill figures into the price purchasers are willing to pay for your property, bad will reduces the premium the property commands Bad will accounts for detractions like poor location, or in the case of tenant turnover, undesirability, low demand, and generally bad karma You may never know the extent of the property s bad reputation, but it could be playing a role in making your property difficult to lease out and, ultimately, to sell Convinced We thought so Respect your tenants, and a stable cash flow follows Don t respect them, and you might not only lose income, but also cause the value of your property to suffer.

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