A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

Prediction in the linear model under a linear constraint

In this paper we determine the Gauss–Markov predictor of the nonobservable part of a random vector satisfying the linear model under a linear constraint.     

Evaluating the power of Minitab's data subsetting lack of fit test in multiple linear regression

Minitab's data subsetting lack of fit test (denoted XLOF) is a combination of Burn and Ryan's test and Utts' test for testing lack of fit in linear regression models. As an alternative to the classical or pure error lack of fit test, it does not require replicates of predictor variables. However, due to the uncertainty about its performance, XLOF still remains unfamiliar to regression users while the well-known classical lack of fit test is not applicable to regression data without replicates. So far this procedure has not been mentioned in any textbooks and has not been included in any other software packages. This study assesses the performance of XLOF in detecting lack of fit in linear regressions without replicates by comparing the power with the classic test. The power of XLOF is simulated using Minitab macros for variables with several forms of curvature. These comparisons lead to pragmatic suggestions on the use of XLOF. The performance of XLOF was shown to be superior to the classical test based on the results. It should be noted that the replicates required for the classical test made itself unavailable for most of the regression data while XLOF can still be as powerful as the classic test even without replicates.     

Estimation and interpretation of models of absolute risk from epidemiologic data,including family-based studies

Absolute risk is the chance that a person with given risk factors and free of the disease of interest at age a will be diagnosed with that disease in the interval (a, a + τ]. Absolute risk is sometimes called cumulative incidence. Absolute risk is a “crude” risk because it is reduced by the chance that the person will die of competing causes of death before developing the disease of interest. Cohort studies admit flexibility in modeling absolute risk, either by allowing covariates to affect the cause-specific relative hazards or to affect the absolute risk itself. An advantage of cause-specific relative risk models is that various data sources can be used to fit the required components. For example, case–control data can be used to estimate relative risk and attributable risk, and these can be combined with registry data on age-specific composite hazard rates for the disease of interest and with national data on competing hazards of mortality to estimate absolute risk. Family-based designs, such as the kin-cohort design and collections of pedigrees with multiple affected individuals can be used to estimate the genotype-specific hazard of disease. Such analyses must be adjusted for ascertainment, and failure to take into account residual familial risk, such as might be induced by unmeasured genetic variants or by unmeasured behavioral or environmental exposures that are correlated within families, can lead to overestimates of mutation-specific absolute risk in the general population.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

Estimation of regression models with equi-correlated responses when some observations on the response variable are missing

The present article deals with the problem of estimation of parameters in a linear regression model when some data on response variable is missing and the responses are equi-correlated. The ordinary least squares and optimal homogeneous predictors are employed to find the imputed values of missing observations. Their efficiency properties are analyzed using the small disturbances asymptotic theory. The estimation of regression coefficients using these imputed values is also considered and a comparison of estimators is presented.     

Marketing in moderation— The marketing concept and the organization's structure

This paper discusses the marketing concept and its impact on the strategy and structure of the business organization. The popular dictum that every firm should implement the marketing concept in its extreme form is challenged. Considerations of the overall structure of the firm, the time period in question, the amount of resources available are included in the determination of the degree of implementation which should be made. Finally, the conclusion indicates that the popular view that a change in environment necessitates a change in the organizational structure should be modified. The cost may be too great for the benefits derived.