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Purpose – The purpose of this chapter is to estimate non-Gaussian distributions by means of Johnson distributions. An empirical illustration on hedge fund returns is detailed.

Methodology/approach – To fit non-Gaussian distributions, the chapter introduces the family of Johnson distributions and its general extensions. We use both parametric and non-parametric approaches. In a first step, we analyze the serial correlation of our sample of hedge fund returns and unsmooth the series to correct the correlations. Then, we estimate the distribution by the standard Johnson system of laws. Finally, we search for a more general distribution of Johnson type, using a non-parametric approach.

Findings – We use data from the indexes Credit Suisse/Tremont Hedge Fund (CSFB/Tremont) provided by Credit Suisse. For the parametric approach, we find that the SU Johnson distribution is the most appropriate, except for the Managed Futures. For the non-parametric approach, we determine the best polynomial approximation of the function characterizing the transformation from the initial Gaussian law to the generalized Johnson distribution.

Originality/value of chapter – These findings are novel since we use an extension of the Johnson distributions to better fit non-Gaussian distributions, in particular in the case of hedge fund returns. We illustrate the power of this methodology that can be further developed in the multidimensional case.

The purpose of this paper is to analyze the development of body fitness equipment in Taiwan from the viewpoint of innovation and the factor of success for innovation.

The research methods include a literature study and a case study. In addition, the innovation activity of an enterprise is based on the viewpoint of procedure (input‐process‐output). The innovation activity of an enterprise is separated into three phases: innovation motivation, innovation process, and innovation performance.

The enterprise's competitive advantage and efficiency can clearly be seen, and the reason for the enterprise's success can easily be identified through a structural analysis of the innovation process. The research results indicate four factors for the success of Johnson: mastering technology, good management, employment of diversified talents, and clear brand positioning.

The innovation research model provides a comprehensive summary of the innovation process, stressing innovation activity. The study of the factors of success does not establish quantified or non‐quantified innovation indicators.

The factor of success of innovation is not exactly the same for every company. Sorting and analyzing the individual cases can serve as a basis of reference and act as a guidepost for other companies in similar industries during their business development.

The analysis model of innovation activity can clearly explore the development process of the innovation success of an enterprise. Simultaneously, this research can serve as a role model for other companies when they innovate.

With the aim of discovering the important factors in training staff to use newly installed automated circulation systems, the appropriate libraries in New Zealand were sent a questionnaire in 1988. This asked how libraries had trained their staff, and the outcome of the training in terms of satisfaction with their decisions, sparseness of mistakes after implementation and time taken for staff to attain efficiency after implementation. The most important factors in training were that the vendor should be heavily involved in organizing and carrying out training; training should be given before the system went live; staff should be given detailed training in the whole system; and a number of different training methods should be used. These factors were especially important for libraries installing their first automated system, those installing integrated systems, those with few staff, and those installing their systems in the earlier years of automation; that is, those libraries whose staff have the least background in the system being installed, have the greatest need for excellent training.

The Online Multiple User System (TOMUS) is an integrated turnkey library system, consisting of both hardware and software. It is modular, operating on multiple microprocessors combined in a unique and efficient manner. Downtime is minimized. Performance can be optimized by positioning additional processors to support specific library functions that require additional computing capacity. Online catalog, cataloging and authority functions are currently supported. Circulation software will be released in late 1986. Other modules will follow soon.

Paint Manufacturer, AB Wilhelm Becker is this year celebrating its 125th Anniversary, with worldwide parties commemorating the event.

The purpose of this study is to research the influence of wire’s surface topography on interwire contact performance of simple spiral strand.

The mechanical model of the simple spiral strand imposed by a tensile load is first established, into which the surface topography, Poisson’s ratio effect and radial deformation are incorporated simultaneously. Meanwhile, the Gaussian and non-Gaussian rough surfaces of the steel wires are obtained with the fast Fourier transform (FFT) and digital filter technology. Then, the rough interwire contact performance of the simple spiral strand is calculated by using conjugate gradient method and FFT.

As compared with smooth wire surface, both the longitudinal orientation for the Gaussian wire surface and large kurtosis or small skewness for the non-Gaussian surface yield a small contact pressure and stress.

This study conducts detailed discussion of the influence of wire’s surface topography on the interwire contact performance for the simple spiral strand and gives a beneficial reference for the design and application of a wire rope.

This paper aims to propose a framework for optimizing the pose in the assembly process of the non-ideal parts considering the manufacturing deviations and contact deformations. Furthermore, the accuracy of the method would be verified by comparing it with the other conventional methods for calculating the optimal assembly pose.

First, the surface morphology of the parts with manufacturing deviations would be modeled to obtain the skin model shapes that can characterize the specific geometric features of the part. The model can provide the basis for the subsequent contact deformation analysis. Second, the simulated non-nominal components are discretized into point cloud data, and the spatial position of the feature points is corrected. Furthermore, the evaluation index to measure the assembly quality has been established, which integrates the contact deformations and the spatial relationship of the non-nominal parts’ key feature points. Third, the improved particle swarm optimization (PSO) algorithm combined with the finite element method is applied to the process of solving the optimal pose of the assembly, and further deformation calculations are conducted based on interference detection. Finally, the feasibility of the optimal pose prediction method is verified by a case.

The proposed method has been well suited to solve the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the effectiveness of the method with an example of the shaft-hole assembly.

The method proposed in this paper has been well suited to the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the method with an example of the shaft-hole assembly.

The different surface morphology influenced by manufacturing deviations will lead to the various contact behaviors of the mating surfaces. The assembly problem for the components with complex geometry is usually accompanied by deformation due to the loading during the contact process, which may further affect the accuracy of the assembly. Traditional approaches often use worst-case methods such as tolerance offsets to analyze and optimize the assembly pose. In this paper, it is able to characterize the specific parts in detail by introducing the skin model shapes represented with the point cloud data. The dynamic changes in the parts' contact during the fitting process are also considered. Using the PSO method that takes into account the contact deformations improve the accuracy by 60.7% over the original method that uses geometric alignment alone. Moreover, it can optimize the range control of the contact to the maximum extent to prevent excessive deformations.

The purpose of this study is to suggest a new framework that we call the CIR#, which allows forecasting interest rates from observed financial market data even when rates are negative. In doing so, we have the objective is to maintain the market volatility structure as well as the analytical tractability of the original CIR model.

The novelty of the proposed methodology consists in using the CIR model to forecast the evolution of interest rates by an appropriate partitioning of the data sample and calibration. The latter is performed by replacing the standard Brownian motion process in the random term of the model with normally distributed standardized residuals of the “optimal” autoregressive integrated moving average (ARIMA) model.

The suggested model is quite powerful for the following reasons. First, the historical market data sample is partitioned into sub-groups to capture all the statistically significant changes of variance in the interest rates. An appropriate translation of market rates to positive values was included in the procedure to overcome the issue of negative/near-to-zero values. Second, this study has introduced a new way of calibrating the CIR model parameters to each sub-group partitioning the actual historical data. The standard Brownian motion process in the random part of the model is replaced with normally distributed standardized residuals of the “optimal” ARIMA model suitably chosen for each sub-group. As a result, exact CIR fitted values to the observed market data are calculated and the computational cost of the numerical procedure is considerably reduced. Third, this work shows that the CIR model is efficient and able to follow very closely the structure of market interest rates (especially for short maturities that, notoriously, are very difficult to handle) and to predict future interest rates better than the original CIR model. As a measure of goodness of fit, this study obtained high values of the statistics R² and small values of the root of the mean square error for each sub-group and the entire data sample.

A limitation is related to the specific dataset as we are examining the period around the 2008 financial crisis for about 5 years and by using monthly data. Future research will show the predictive power of the model by extending the dataset in terms of frequency and size.

Improved ability to model/forecast interest rates.

The original value consists in turning the CIR from modeling instantaneous spot rates to forecasting any rate of the yield curve.

The purpose of this article is to derive inference for multicomponent reliability where stress-strength variables follow unit generalized Rayleigh (GR) distributions with common scale parameter.

The authors derive inference for the unknown parametric function using classical and Bayesian approaches. In sequel, (weighted) least square (LS) and maximum product of spacing methods are used to estimate the reliability. Bootstrapping is also considered for this purpose. Bayesian inference is derived under gamma prior distributions. In consequence credible intervals are constructed. For the known common scale, unbiased estimator is obtained and is compared with the corresponding exact Bayes estimate.

Different point and interval estimators of the reliability are examined using Monte Carlo simulations for different sample sizes. In summary, the authors observe that Bayes estimators obtained using gamma prior distributions perform well compared to the other studied estimators. The average length (AL) of highest posterior density (HPD) interval remains shorter than other proposed intervals. Further coverage probabilities of all the intervals are reasonably satisfactory. A data analysis is also presented in support of studied estimation methods. It is noted that proposed methods work good for the considered estimation problem.

In the literature various probability distributions which are often analyzed in life test studies are mostly unbounded in nature, that is, their support of positive probabilities lie in infinite interval. This class of distributions includes generalized exponential, Burr family, gamma, lognormal and Weibull models, among others. In many situations the authors need to analyze data which lie in bounded interval like average height of individual, survival time from a disease, income per-capita etc. Thus use of probability models with support on finite intervals becomes inevitable. The authors have investigated stress-strength reliability based on unit GR distribution. Useful comments are obtained based on the numerical study.

This research aims to contribute to the literature on Lean implementation in healthcare by studying the emotional experiences of the relevant actors related to a Rapid Process Improvement Workshop (RPIW) in a UK healthcare context. The purpose of this study was to go beyond what people think about Lean and towards an exploration of their subjective, emotional and “feeling” experience and whether that emotional experience influenced Lean implementation.

A phenomenological and symbolic interactionist qualitative case study was undertaken. Data related to participants' emotional experience were collected through non-participant observation and semi-structured interviews. Data were analysed using thematic network analysis.

This paper provides novel insights into the emotional experience of Lean as experienced through an RPIW. The findings reveal that participation in an RPIW is much more than a technical process. It influences how people feel about themselves, is based on relationships with others, and requires mental, physical and emotional effort. All of these factors influence engagement with, initiation of and sustainability of the RPIW.

A new conceptual framework for the planning and implementation of RPIWs has been developed. However, because of the chosen research approach, the results may lack generalisability. Therefore, researchers are encouraged to test the framework and proposed practice implications.

Despite emotions being an integral part of individual and social everyday life, emotional experience has not been studied in relation to Lean. This study is the first to explore emotions in relation to Lean, with implications for practice as to how RPIWs are managed with a new framework for implementation being proposed.