General Linear Statistical Model Research Articles

Evaluation of bulky DNA adduct levels after pesticide use: Comparison between open-field farmers and fruit growers

Genotoxic impact from pesticide use was evaluated and compared between open-field farmers and fruit growers. Groups of farmers were classified according to the main pesticide sprayed (triazoles or chlorothalonil for open-field farmers n = 19; captan for fruit growers n = 29). Two blood samples (S1 and S3) were collected on consecutive days for each farmer, and white blood cell bulky DNA adduct levels were evaluated by 32P-postlabelling method. Exposure to pesticides was estimated using a questionnaire. Within each group, no significant variation in the mean relative adduct level (RAL) values was observed between S1 and S3. A quantitative increase was observed at S3 for the subgroup of farmers exposed to chlorothalonil and associated insecticides. Between groups, the mean RAL was significantly higher for open-field farmers, a difference essentially attributable to the triazole subgroup. Owing to the sampling chronology (farmers exposed to triazoles were sampled first, several weeks before farmers exposed to chlorothalonil and associated insecticides), seasonal variations may account for changes in DNA adduct levels in open-field farmers. Among fruit growers, a significant relationship was found between RALS3 and two exposure variables generated by questionnaires (statistical general linear model).

Experimental determination of viability loss of Penicillium bilaiae conidia during convective air-drying

A study was conducted on the drying of Penicillium bilaiae, a fungal micro-organism used to promote soil-bound phosphorous uptake in several crop species, such as wheat, canola and pulse crops. A wet pellet formed from a mixture of the inoculant and a starch-based carrier was air-dried to the appropriate water activity to extend the shelf-life of the viable fungal conidia. Convective air-drying was examined as a low-energy alternative to the more expensive freeze-drying technology that is currently in use. Experiments were conducted to measure the loss of conidia viability during drying in a fixed-bed, thin-layer convective dryer. The dryer air inlet temperature and relative humidity were controlled in experiments to determine the effect of thermal and dessicative stresses on conidial viability. The measured survivor fraction was determined to be dependent on solids temperature, moisture content and drying rate. Thermal stresses became significant for process temperatures above 30 degrees C, while the survivor fraction fell sharply below a dry basis moisture ratio of 30%. Slower drying kinetics associated with high inlet air relative humidity were found to significantly improve the recovery of viable conidia. By minimising environmental stresses, survivor fractions of up to 75% could be achieved, but this result fell dramatically with the introduction of more severe conditions. A general linear statistical model is used to quantify experimental error and the significance level of each factor.

Sex- and age-dependent human transcriptome variability: implications for chronic heart failure.

Heart failure (HF) is the end result of progressive and diverse biological adaptations within the diseased myocardium. We used cDNA microarrays and quantitative PCR to examine the transcriptomes of 38 left ventricles from failing and nonfailing human myocardium. After identification of a pool of putative HF-responsive candidate genes by microarrays on seven nonfailing and eight failing hearts, we used quantitative PCR and a general linear statistical model in a larger sample set (n = 34) to validate and examine the role of contributing biological variables (age and sex). We find that most HF-candidate genes (transcription factors, Cebpb, Npat; signaling molecules, Map2k3, Map4k5; extracellular matrix proteins, Lum, Cola1; and metabolic enzymes, Mars) demonstrated significant changes in gene expression; however, the majority of differences among samples depended on variables such as sex and age, and not on HF alone. Some HF-responsive gene products also demonstrated highly significant changes in expression as a function of age and/or sex, but independent of HF (Ngp1, Cd163, and Npat). These results emphasize the need to account for biological variables (HF, sex and age interactions) to elucidate genomic correlates that trigger molecular pathways responsible for the progression of HF syndromes.

Generalized moment based estimation and inference

In this paper we consider the case of the general linear statistical model, where the stochastic design matrix has the property that its expectation and probability limits with the vector of unobserved disturbances does not equal to a zero vector and moreover, the explanatory and/or instrumental variables are ill-conditioned. In this context, we contemplate generalized moment based estimating functions that may be biased, and for which the corresponding system of moment conditions are overdetermined. To cope with this situation, we propose a new estimator with attractive finite and asymptotic sampling properties and that result in entropic densities on model parameters.

A mechanistic evaluation of modified asphalt paving mixtures

The main purpose of this study was to facilitate decisions concerning the effectiveness of modifiers in mitigating pavement distress and improving long-term overall pavement performance in actual field conditions, by utilizing short-term laboratory results and a mathematical prediction model. The modifiers investigated were carbon black, neoprene latex, and polymer modified asphalt (STYRELF). The statistical general linear model (GLM) and the Fisher least significant difference (LSD) were used for the analysis of data. The results of the study indicate that the effect of the modifier on the paving mixture properties was insignificant at low temperatures (down to −17 °C), but significant at high temperatures (up to 60 °C) where the synergistic effect of the modifier on the paving mixture was pronounced. The VESYS IIIA pavement performance prediction model was utilized to assess the effects, if any, of the modifier on the pavement's overall performance. All the modifiers improve, to some degree, the overall pavement performance. Key words: modifiers, asphalt, paving mixtures, pavements, polymer asphalt.

Improved estimation under collinearity and squared error loss

This paper examines the performance of several biased, Stein-like and empirical Bayes estimators for the general linear statistical model under conditions of collinearity. A new criterion for deleting principal components, based on an unbiased estimator of risk, is introduced. Using a squared error measure and Monte Carlo sampling experiments, the resulting estimator's performance is evaluated and compared with other traditional and non-traditional estimators.

Sampling Performance of Some Joint One-Sided Preliminary Test Estimators under Squared Error Loss

IN MANY AREAS OF SCIENCE, when drawing conclusions concerning a set of phenomena, individual or linear combinations of parameters are assumed to be nonnegative, nonpositive, or to lie between upper and lower bounds. Likewise, many functions are assumed to be monotonic, convex, or quasiconvex. Consequently, questions naturally arise as to the correctness of the assumptions and, within the context of statistical inference, how to evaluate the evidence against these assumptions and to use the data at hand to seek the appropriate statistical model and the corresponding method of estimation. Within the context of the general linear statistical model, multivariate analogues of one-sided (inequality hypotheses) tests have been explored by Bartholomew (1959), Kudo (1963), Osterhoff (1969), Barlow, et al. (1972), Yancey, Judge, and Bock (1981), Yancey, Bohrer, and Judge (1982), Gourieroux, Holly, and Montfort (GHM) (1982), Judge and Yancey (1986), Wolak (1987) and Shapiro (1988). In general, the likelihood ratio framework has been used in developing a test statistic and determining acceptance and rejection regions. In order to facilitate the interpretation of these test results in econometric practice, in this paper we evaluate, under a squared error loss (SEL) measure, the risk characteristics of the preliminary test (PT) estimators that evolve when, based on the data at hand, an estimation decision is taken as a result of the outcomes of two one-sided multivariate hypothesis tests.

Monitoring workplace safety: Nonlinear optimization in a statistical model

Given a cluster of enclosed work areas, the problem is to maintain frequent monitoring of the atmosphere in each area for hazardous levels of contaminants. A central mass spectrometer is capable of doing this monitoring, provided it can be used efficiently. Mathematically, a mass spectrometer may be modeled by a general linear statistical model where the unknowns are the concentrations of the possible contaminants and the observations are the gate readings of the number of ions with a chosen mass/charge ratio. The accuracy of the observations depends on time, and the necessity for frequent monitoring imposes a time constraint. The mathematical problem can be formulated as a problem in the theory of optimal experimental designs. This paper will present a theoretical solution to the problem, an algorithm to implement that solution and some empirical results obtained from the implementation.

Improved Methods of Inference in Econometrics.

Introduction. The Inferential and Decision Framework. The Measure of Performance. Some Alternative Statistical Models, Estimators and Tests. Inequality Estimation and Hypothesis Testing. Inequality Estimation and Hypothesis Testing of the Location Parameter of a Normal Random Variable. General Linear Statistical Model and a Single Linear Inequality Constraint. Inequality Restricted Estimation of Two or More Location Parameters: The Orthonormal Case. An Inequality Restricted Stein Rule Estimator. Hypothesis Testing for Two or More Inequalities: The Orthogonal Case. Inequality Restricted Estimation: General Design and Restriction Matrices. Inequality Hypothesis Testing: General Case. Some Sampling Results for the Stein Family of Estimators. Assessing the Precision of Stein's Estimator. Some Evaluations of the Sampling Performance of the Limited Translation and New Stein Estimators. Estimation Under Non-Normal Errors and Quadratic Loss. Estimation and Hypothesis Testing in the Case of Possible Heteroskedasticity. Subject Index.

On estimation of diagonal covariance matrices by minque

Let Y = Xβ + ϵ be a general linear statistical model with co-variance matrix of the form , where δi, is a known diagonal matrix and σi is an unknown scalar. This form includes the I common linear regression model with uncorrelated observations, with or without replications; the random coefficients model of Hildreth and Houck (1968); and numerous mixed effects models after a suit-able diagonalizing transformation. The MINQUE of is derived by a simple, intuitive approach in which a weighted least squares criterion is applied to the vector of squared residuals. In addition to providing a different perspective, this approach has the advantage that estimation can be restricted to the known parameter space. The restricted estimates are always nonnegative, but still equal to the MINQUE values when the latter are also nonnegative. A hypothetical example is simulated in which the restricted and unrestricted estimates are compared. The practical significance for an iterative feedback procedure is discussed, and som...

PRIOR ADJUSTMENT: AN EXTENSION OF THE FRISCH‐WAUGH THEOREM TO THE METHOD OF “TWO‐STAGE LEAST‐SQUARES”

The problems of seasonal adjustment and other forms of prior adjustment have seldom been integrated into a general framework of estimation. A well-known result, due to Frisch and Waugh, has been used to demonstrate how linear seasonal influences might be treated in the context of the general linear statistical model. In this paper, the author establishes a proposition that one form of prior adjustment is consistent with two different estimating techniques that are in common use.

Seasonal Adjustment of Economic Data by Application of the General Linear Statistical Model

Abstract This article presents a technique of seasonal adjustment of economic time series within the framework of the general linear statistical model. The technique is designed to be flexible enough to be applicable to varying types of economic time series. The first goal of the technique is the development of a specification that allows adequate flexibility in the estimation of the trend-cycle component of a time series. The second goal is the development of a method of handling changing seasonality. The results of applying the technique to simulated time series are presented. These are compared with the results of applying the Census X-11 method.

Seasonal Adjustment of Economic Data by Application of the General Linear Statistical Model

Abstract This article presents a technique of seasonal adjustment of economic time series within the framework of the general linear statistical model. The technique is designed to be flexible enough to be applicable to varying types of economic time series. The first goal of the technique is the development of a specification that allows adequate flexibility in the estimation of the trend-cycle component of a time series. The second goal is the development of a method of handling changing seasonality. The results of applying the technique to simulated time series are presented. These are compared with the results of applying the Census X-11 method.

Seasonal Adjustment and Forecasting in the Presence of Trend

A general linear statistical model for simultaneous seasonal adjustment and trend estimation is considered for one and two term deterministic trend functions. Explicit estimates of the parameters and variances are derived in a convenient computational form from which the properties of these estimates become apparent. In connection with possible uses of the model for forecasting, the Smallest Neighbourhood (SN) is introduced, within which the trend is assumed to be either linear or representable by a single term. Examples are given to show that simple techniques in certain situations may yield accurate forecasts in return for a comparatively modest amount of computational effort. A procedure season is given which calculates, for any general one term trend function, estimates of seasonal and trend constants together with standard errors and provides predictions, also with standard errors, for any required period.

Minimum Variance, Linear, Unbiased Seasonal Adjustment of Economic Time Series

Abstract A statistical theory based on the general linear statistical model is developed for seasonal adjustment of economic (and other) time series. A method for seasonal adjustment may be represented as taking place in two steps. The first step is to estimate the unknown parameters of the seasonal component of the series; the second step is to remove the estimated seasonal component from the set of observations. For the unique minimum variance, linear, unbiased method for seasonal adjustment, estimation is carried out through the unique, minimum variance, linear unbiased estimator. Sampling theory for statistical inference about a method for seasonal adjustment may be derived from normal sampling theory for the general linear statistical model. The properties of minimum variance, linearity, and unbiasedness provide a complete basis for the selection of a method for seasonal adjustment.

Minimum Variance, Linear, Unbiased Seasonal Adjustment of Economic Time Series

Abstract A statistical theory based on the general linear statistical model is developed for seasonal adjustment of economic (and other) time series. A method for seasonal adjustment may be represented as taking place in two steps. The first step is to estimate the unknown parameters of the seasonal component of the series; the second step is to remove the estimated seasonal component from the set of observations. For the unique minimum variance, linear, unbiased method for seasonal adjustment, estimation is carried out through the unique, minimum variance, linear unbiased estimator. Sampling theory for statistical inference about a method for seasonal adjustment may be derived from normal sampling theory for the general linear statistical model. The properties of minimum variance, linearity, and unbiasedness provide a complete basis for the selection of a method for seasonal adjustment.