Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

Proportional reversed hazard rate model and its applications

The purpose of this paper is to study the structure and properties of the proportional reversed hazard rate model (PRHRM) in contrast to the celebrated proportional hazard model (PHM). The monotonicity of the hazard rate and the reversed hazard rate of the model is investigated. Some criteria of aging are presented and the inheritance of the aging notions (of the base distribution) by the PRHRM is studied. Characterizations of the model involving Fisher information are presented and the statistical inference of the parameters is discussed. Finally, it is shown that several members of the proportional reversed hazard rate class have been found to be useful and flexible in real data analysis.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

Horwitz's rule, transforming both sides and the design of experiments for mechanistic models

Summary. The paper develops methods for the design of experiments for mechanistic models when the response must be transformed to achieve symmetry and constant variance. The power transformation that is used is partially justified by a rule in analytical chemistry. Because of the nature of the relationship between the response and the mechanistic model, it is necessary to transform both sides of the model. Expressions are given for the parameter sensitivities in the transformed model and examples are given of optimum designs, not only for single-response models, but also for experiments in which multivariate responses are measured and for experiments in which the model is defined by a set of differential equations which cannot be solved analytically. The extension to designs for checking models is discussed.     

How Welfare Policies Affect Adolescents' School Outcomes: A Synthesis of Evidence From Experimental Studies

Using data from 8 random assignment studies and employing meta‐analytic techniques, this article provides systematic evidence that welfare and work policies targeted at low‐income parents have small adverse effects on some school outcomes among adolescents ages 12 to 18 years at follow‐up. These adverse effects were observed mostly for school performance outcomes and occurred in programs that required mothers to work or participate in employment‐related activities and those that encouraged mothers to work voluntarily. The most pronounced negative effects on school outcomes occurred for the group of adolescents who had a younger sibling, possibly because of the increased home and sibling care responsibilities they assumed as their mothers increased their employment.     

A general inverse sampling scheme and its application to adaptive cluster sampling

Not having a variance estimator is a seriously weak point of a sampling design from a practical perspective. This paper provides unbiased variance estimators for several sampling designs based on inverse sampling, both with and without an adaptive component. It proposes a new design, which is called the general inverse sampling design, that avoids sampling an infeasibly large number of units. The paper provide estimators for this design as well as its adaptive modification. A simple artificial example is used to demonstrate the computations. The adaptive and non‐adaptive designs are compared using simulations based on real data sets. The results indicate that, for appropriate populations, the adaptive version can have a substantial variance reduction compared with the non‐adaptive version. Also, adaptive general inverse sampling with a limitation on the initial sample size has a greater variance reduction than without the limitation.     

Estimation of Metabolic Rate Constants in PBPK‐Models from Liver Slice Experiments: What Are the Experimental Needs?

A mechanistic model is presented describing the clearance of a compound in a precision-cut liver slice that is incubated in a culture medium. The problem of estimating metabolic rate constants in PBPK models from liver slice experiments is discussed using identifiability analysis. From the identifiability problem analysis, it appears that in addition to the clearance, the compound's free fraction in the slice and the diffusion rate of the exchange of the compound between culture medium and liver slice should be identified. In addition, knowledge of the culture medium volume, the slice volume, the compound's free fraction, and octanol-water-based partition between medium and slice is presupposed. The formal solution for identification is discussed from the perspective of experimental practice. A formally necessary condition for identification is the sampling of parent compound in liver slice or culture medium. However, due to experimental limitations and errors, sampling the parent compound in the slice together with additional sampling of metabolite pooled from the medium and the slice is required for identification in practice. Moreover, it appears that identification results are unreliable when the value of the intrinsic clearance exceeds the value of the diffusion coefficient, a condition to be verified a posteriori.     

Vulnerability and Agenda: Context and Process in Project Management

This paper explores how the project management role is conditioned by the context in which change is progressed. The argument draws on Pettigrew's (1985, 1987) contextual and processual view of change, a view which does not clarify the management implications of contextual variation. Using data from a two-stage research design combining diary and survey methods, four context dimensions are identified, concerning ‘interlocking’, ‘shifting sands’, ‘ownership’ and ‘senior management view’. For analytical purposes, two extreme contexts are characterized as exposing the project manager to high and low levels of ‘vulnerability’ respectively. Project management literature typically offers a rational-linear account of change, and concentrates on ‘content’ and ‘control’ agendas, concerning technical expertise on the one hand, and planning, budgeting and monitoring techniques on the other. Sociological analyses reveal the limitations of the rational-linear account, and focus on the political and cultural dimensions of the ‘process’ agenda. The data suggest how contextual variation affects the relative priority of these agendas. The management implications of these findings are explored, for project management selection, career progression, and the development of diagnostic skills and ‘agenda management’ strategies.     

Student perceptions of the danger of drug use: a factor analysis.

Australian secondary school students from three Year levels were asked to rate the danger involved in various uses of alcohol, tobacco, inhalants, marijuana and other illicit drugs. These ratings were then factor analyzed for each Year level. The results showed a more refined discrimination among the older students, with type of drug and frequency of use being important considerations. Familiarity was a major consideration for the younger students. The cognitive factors derived from the data are consistent with expectations based on the gateway theory of drug use.