The influence of the birthplace and models of care on midwifery practice for the management of women in labour

This paper will examine how the settings in which midwives practice (the birthplace) and models of care affect midwives’ decision making during the management of labour. One-hundred-and-four independent, team and hospital based midwives and 100 low obstetric risk nulliparous women to whom labour care was provided were surveyed. These midwives and women resided in the Auckland metropolitan area of New Zealand. The majority of midwives who participated worked in models of care which provided women with continuity of carer and care, however, this was not found to influence the way the midwives provided labour care. Instead, practice was found to be relatively homogenous regardless of whether the midwives worked in independent, team, or hospital-based practice. The birthplace setting in which the labour care took place did influence midwifery practice. The majority of midwives provided labour care in large obstetric hospitals and identified practices dominated by the medical model of care. Practice was described as being influenced by intervention and the need for technology, however, this did not prevent the majority of women from perceiving they were actively involved in the decision making process and that they worked in partnership with their midwives. Closer examination of the midwives’ decision making processes whilst providing the labour care revealed that the midwives’ individual decisions were influenced by the needs of the women rather than the hospital protocols. What became evident was that the midwives in this study had adopted a humanistic approach to care whereby technology was used alongside relationship-centred care.     

Comparing volatility forecasting models during the global financial crisis

Volatility estimation in financial markets has always been a challenge especially in time of crisis. Once asset prices and investment decisions are highly sensitive to such variable, many different models have been proposed in literature. This article estimates the volatility from a new family of stochastic volatility models called non-Gaussian State Space Models, a subclass of state space models where it is possible to compute exact likelihood. Volatilities of important Asian and Oceanian stock market indexes have been estimated and compared to APARCH model estimates. Results showed that non-Gaussian State Space Models outperformed significantly in both in-sample and forecasting cases.     

Ramifications of Monitoring Service Quality Through Passively Solicited Customer Feedback*

It is common for service providers to collect data from customers as part of efforts to monitor quality. Often, this data is passively collected, meaning (a) any solicitation of feedback is done without direct customer interaction, and (b) the customer initiates any response given. Examples include customer comment cards, toll-free telephone numbers, and comment links on World Wide Web pages. This article compares passive data collection with active methods (e.g., interviews and mail surveys). Passive methods generally have lower response rates and are inherently biased, but have cost and sample frame advantages when used to monitor quality on a continuous basis. Despite the biased nature of passive methods, this article describes the successful validation of a common customer-response model with passively collected empirical data. The model is expanded to consider the impact of complaint and compliment solicitation on customers' evaluation of the service provider. Results show that this impact is negative, and that customers who spontaneously register complaints generally record higher ratings of the service provider than customers who complain in response to a complaint solicitation. Discussion and conclusions are given.     

Artificial Neural Network Models for Pricing Initial Public Offerings

In recent times, managerial applications of neural networks, especially in the area of financial services, has received considerable attention. In this paper, neural network models are developed for a new application: the pricing of Initial Public Offerings (IPOs). Previous empirical studies provide consistent evidence of considerable inefficiency in the pricing of new issues. Neural network models using publicly available financial data as inputs are developed to price IPOs. The pricing performance and the economic benefits of the neural network models are evaluated. Significant economic gains are documented with neural networks. Several tests to establish generalizability and robustness of the results are conducted.     

Heuristic Procedures for Practical-Sized Uncapacitated Location-Capacitated Routing Problems*

Facility location and vehicle routing are two important logistical problems closely interrelated in many real-world applications where locating facilities and determining the associated multi-stop vehicle routes are required simultaneously. Previous research has found that using the classical facility location models on these location-routing problems (LRPs) may lead to suboptimal solutions. We propose an approximate approach for the LRPs, which first generates and improves feasible location/allocation schemes with the associated multi-stop routing costs approximated using some route length estimators. We then design the minimum-cost routes based on the location/allocation results. We review two estimators that can provide accurate approximations to the multi-stop route distances; define the uncapacitated location-capacitated routing problem; and evaluate several heuristic procedures for approximately solving the problem. Computational results show that when vehicle capacities are not too restrictive, the sequential procedures that incorporate the two robust route length estimators can produce good solutions to practical-sized problems with a reasonable amount of computational efforts.     

Product Positioning and Pricing Under Production Cost Considerations*

A framework in a competitive environment is proposed that incorporates production cost and economies of scale in the problem of positioning a product for a market segment. The model facilitates the existence of a Nash equilibrium in prices and product positions. As such, firms can simultaneously choose prices and product positions for the segment. This result improves the traditional theory on equilibria points in prices and product positions where firms choose their product positions first and then set their prices. A sensitivity analysis demonstrates the effects of changes in the unit savings derived from economies of scale or the cost of furnishing a product with its attributes by one firm on the product positions, prices, and profits of all competing firms. More important, the paper examines the effect on prices and profits of competing firms when one of the firms repositions its product closer to the segment's ideal point. It is shown that under certain conditions, the profit of a firm may actually decrease as it redesigns its product closer to the segment's ideal point. These conditions assist management to identify the product design beyond which enhancements of the product would lead to lower profits because of increasing production costs. It is also shown that the price of this firm increases. Past research supports the idea that positioning a brand closer to the ideal point, given fixed product positions of competing firms, would lead to greater buyer preferences and eventually higher profits. The price and profits of the competing firm may increase or decrease. Conditions are derived under which a movement towards the segment's ideal point by one firm would lead to higher profits by the competing firm.     

广义部分线性单指数模型的惩罚样条估计

 本文讨论了指数族广义部分线性单指数模型(Generalized Partially Linear Single Index Models, GPLSIM) 的惩罚样条迭代估计,提出了基于惩罚似然和一组预先取定的单指数参数向量 的初始估计的迭代估计算法。另外本文还通过一组模拟数据的分析对所提出的迭代算法进行了验证。     

Unacceptable Solutions and the Hybrid Discriminant Model

A new family of linear programming (LP) discriminant models, the hybrid discriminant model, was recently presented with claims that it avoids all the problems of its predecessor LP models. However, this note shows that it does not avoid unacceptable solutions.     

Optimal Cash Tender Offers*

This paper develops a contingent claims analysis, a simple decision model for determining the optimal price in a cash tender offer. The implied behavior of the optimal tender offer premium over the market price of the target common stock is also investigated. Those readers interested in applying the model may obtain an interactive computer program written in FORTRAN and implementable on a personal computer from the authors on request.     

AN EMPIRICAL ANALYSIS OF THE TAX COURT'S DETERMINATION OF REASONABLE COMPENSATION

The determination of reasonable compensation is one of the most frequently contested issues between the taxpayer and the IRS. The major purpose of this study is to develop a multiple regression model to predict accurately the amount of compensation allowed by the Tax Court as a percent of the amount in dispute between the taxpayer and the IRS. In general, the taxpayer receives favorable treatment in Tax Court when contesting unreasonable compensation payments. The multiple regression model, developed using a stepwise procedure, is a good predictor of the compensation allowed by the court. The overall results have important implications for developing taxpayers' appeal strategies.