A KNOWLEDGE-BASED DSS FOR MANAGERIAL PROBLEM DIAGNOSIS

A knowledge-based system supporting managerial problem diagnosis is described. The system provides the capability to monitor values of selected variables for problem situations. When problems are located, a list of problem symptoms is delivered to a problem processor for structuring and diagnosis. Problem structuring is based on a combination of concepts from expert systems and structural modeling. User assertions about cause-effect relationships between pairs of variables are maintained in a semantic network. Problem diagnosis uses the relationships in the semantic network to construct causation trees, the branches of which represent potential explanations of the problem symptoms. Mathematical models are constructed based on causation-tree branches, and values from the data base are used to test whether the model confirms the diagnosis. If so, the source of the problem has been located and it is then up to the user to resolve the problem. If the model fails to explain the problem, the model apparently is deficient and the user may perform “what if…” type scenarios in attempts to improve the model and search for problem causes. Realistic applications in the accounting and health care areas are discussed.     

FORECAST EVALUATION OF LIVINGSTON DATA ON INFLATION RATES AND INFLATION UNCERTAINTY*

Livingston data on expected inflation rates have been used extensively in the financial literature, especially in investgating the relationship between nominal interest rates and expected inflation rates; however, the data have not been subjected to a thorough analysis of forecast accuracy. The objective of this paper is to analyze the forecast properties of Livingston data using a wide variety of time and frequency domain methods over an extended sample period and for selected subperiods.     

The Predicament of Population Aging: A Review Essay*

Ken Dychtwald, Age Power: How the 21st Century Will Be Ruled by the New Old Richard Leete (ed.), Dynamics of Values in Fertility Change Donald T. Critchlow, Intended Consequences: Birth Control, Abortion, and the Federal Government in Modern America Bernard Jeune and James W. Vaupel (eds.), Validation of Exceptional Longevity     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Emerging Genetic and Epigenetic Mechanisms Underlying Pubertal Maturation in Adolescence

The adolescent transition begins with the onset of puberty which, upstream in the brain, is initiated by the gonadotropin‐releasing hormone (GnRH) pulse generator that activates the release of peripheral sex hormones. Substantial research in human and animal models has revealed a myriad of cellular networks and heritable genes that control the GnRH pulse generator allowing the individual to begin the process of reproductive competence and sexual maturation. Here, we review the latest knowledge in neuroendocrine pubertal research with emphasis on genetic and epigenetic mechanisms underlying the pubertal transition.     

Zero-inflated sum of Conway-Maxwell-Poissons (ZISCMP) regression

While excess zeros are often thought to cause data over-dispersion (i.e. when the variance exceeds the mean), this implication is not absolute. One should instead consider a flexible class of distributions that can address data dispersion along with excess zeros. This work develops a zero-inflated sum-of-Conway-Maxwell-Poissons (ZISCMP) regression as a flexible analysis tool to model count data that express significant data dispersion and contain excess zeros. This class of models contains several special case zero-inflated regressions, including zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), zero-inflated binomial (ZIB), and the zero-inflated Conway-Maxwell-Poisson (ZICMP). Through simulated and real data examples, we demonstrate class flexibility and usefulness. We further utilize it to analyze shark species data from Australia's Great Barrier Reef to assess the environmental impact of human action on the number of various species of sharks.     

Quantile regression for large-scale data via sparse exponential transform method

In recent decades, quantile regression has received much more attention from academics and practitioners. However, most of existing computational algorithms are only effective for small or moderate size problems. They cannot solve quantile regression with large-scale data reliably and efficiently. To this end, we propose a new algorithm to implement quantile regression on large-scale data using the sparse exponential transform (SET) method. This algorithm mainly constructs a well-conditioned basis and a sampling matrix to reduce the number of observations. It then solves a quantile regression problem on this reduced matrix and obtains an approximate solution. Through simulation studies and empirical analysis of a 5% sample of the US 2000 Census data, we demonstrate efficiency of the SET-based algorithm. Numerical results indicate that our new algorithm is effective in terms of computation time and performs well for large-scale quantile regression.     

Participation in Everyday Life Before and After a Housing Adaptation

Housing adaptation aims to enable clients to live independently in their own homes. Studies focusing on participation in everyday life following a housing adaptation are lacking and needed. This study aimed to explore housing adaptation clients' experiences of participation in everyday life before and after a housing adaptation, through the lens of a housing adaptation, using a qualitative follow-up design, with 11 participants. It was found that when the housing adaptation met the participants' needs, performance of activities improved and the housing adaptation opened doors to engagement and participation in everyday life. Thus, focus on performance and engagement in everyday life at the onset of the housing adaptation process, combined with regular follow-ups, may enhance participation.