Variables nuisance

Various protective instruments are used to provide a shutdown signal (to a fast-acting trip valve at the expander inlet) that senses various things, such as overspeed, lubricant pressure, bearing temperature, lubricant temperature, shaft runout, icing, lubricant level, thrustbearing load, and process variables such as sensitive temperatures, levels, pressures, etc. However, too many safety shutdown devices may lead to excessive nuisance shutdowns. 

Harris et al. also employed a less-known CCK procedure, which Meehl and Yonce (1996) named SQUABAC, but the authors referred to as the Parabolic Function Method. Two SQUABAC analyses were performed, one with the PCL-R total score as the input variable and criminal recidivism as the output variable, and another with adult criminal history and recidivism as input and output variables, respectively. Recidivism history was paired with the two potential taxon indicators because it is a conceptually related but distinct variable. It is expected to be a valid indicator of the taxon, but it is not redundant with other indicators, thus nuisance correlations should not be a problem. 

My second example is just as egregious in its own way. White (1982), in what is otherwise an exemplary and still valuable meta-analysis, discusses the use of SES both as a spurious influence (covariate or nuisance variable) and as a causal agent (p. 462). He completely fails to warn the reader, however, that it is mandatory to sort correlations into those based on relationships between biological relatives and those based on relationships between adopted parents and their adopted children. His failure to discuss such a critical issue is perplexing as he does cite two classic adoption studies (Burks, 1928a Freeman, Holzinger, Mitchell, 1928) but not that of Scarr and Weinberg published in 1978. 

Meehl, P. E. (1970). Nuisance variables and the ex post facto design. In M. Radner S. Winokur (Eds.), Minnesota Studies in the Philosophy of Science IV Minneapolis University of Minnesota Press. 

Nonetheless, like most other aspects of odour control, there remain unanswered questions. Chief among these is the relationship between odour potential of a sludge, and the actual level of nuisance found during, say, application to land. Elucidating this relationship requires fairly extensive surveys, because of the variability of weather conditions. Paradoxically, such surveys would depend for their validity on the air sampling methods used the very source of inaccuracy that the Odom Potential test was developed to circumvent. 

TDM can detect interindividual variability in pharmacokinetics that can determine clinical outcome (83, 84, 85, 86, 87, 88 and 89). It is essentially a refinement of the traditional approach of adjusting dose based on clinical response. Using this strategy, the clinician titrates the dose based on an assessment of efficacy versus the development of nuisance or toxic effects. The discussion in this chapter has enumerated those pharmacokinetic factors that may produce variable clinical outcomes in different patients taking the same medication. TDM can be used to detect those differences among patients to guide rational dose adjustment. 

Although intraindividual kinetic variability has only been regarded as a nuisance, the typical degree of intraindividual kinetic variability from all causes can be used to fix rational limits on the increments for tablet dosage, and on permissible tablet-to-tablet and lot-to-lot variability. 

Consider now robustness. If the estimators A are computed from independent response variables then, as noted in Section 1, the estimators have equal variances and are usually at least approximately normal. Thus the usual assumptions, that estimators are normally distributed with equal variances, are approximately valid and we say that there is inherent robustness to these assumptions. However, the notion of robust methods of analysis for orthogonal saturated designs refers to something more. When making inferences about any effect A, all of the other effects At (k i) are regarded as nuisance parameters and robust means that the inference procedures work well, even when several of the effects ft are large in absolute value. Lenth s method is robust because the pseudo standard error is based on the median absolute estimate and hence is not affected by a few large absolute effect estimates. The method would still be robust even if one used the initial estimate 6 of op, rather than the adaptive estimator 6L, for the same reason. 

SAFETY PROFILE Variable toxicity depending upon composition. Cause local irritation of eyes, nose, throat, and lungs. Some may lead to chronic bronchitis, emphysema, and bronchial asthma. Dermatitis may result from short contact. Asthma, angioneurotic edema, hives, etc., may result from short periods of inhalation. A topic eczema, angioneurotic edema, hives, etc., may also result from prolonged contact. A common air contaminant. Nuisance aerosols do evoke some tissue response in the lung upon inhalation of sufficient amounts. However, this reaction is potentially reversible and leaves no scar tissue. 

These are uncontrolled variables which cause differences in the value of y independently of the value of x, resulting in random variation. Nuisance variables are not common in chemistry except where molecules from natural sources are used. To reduce and assess the consequences of nuisance variables ... 

These data provide strong support for the notion that g is composed of independent processes united into a single system. The problem with these data is, of course, that they are from rats. However, in at least one sense, this is also the strength of the data. Laboratory rats are bred for minimum variability. Most scientists find individual differences to be a nuisance and would prefer not to deal with them. That Thompson et al (1990) were able to find the results they did even in rats with individual differences bred out is a tribute to the robustness of the effect. 

Random effects are variables whose levels do not exhaust the set of possible levels and each level is equally representative of other levels. Random effects often represent nuisance variables whose precise value are not usually of interest, but are arbitrary samples from a larger pool of other equally possible samples. In other words, if it makes no difference to the researcher which specific levels of a factor are used in an experiment, it is best to treat that variable as a random effect. The most commonly seen random effect in clinical research are the subjects used in an experiment since in most cases researchers are not specifically interested in the particular set of subjects that were used in a study, but are more interested in generalizing the results from a study to the population at large. 

On the other hand, there may be cases where a random effect is included in the model but not in the mean model. One case would be in a designed experiment where subjects were first randomized into blocks to control variability before assignments to treatments. The blocks the subjects were assigned to are not necessarily of interest—they are nuisance variables. In this case, blocks could be treated as a random effect and not be included in the mean model. When the subject level covariates are categorical (class) variables, such as race, treating random effects beyond random intercepts, which allows each subject to have their own unique baseline, is not usually done. 

On the other hand, it is sometimes seen in the literature that the estimation of the random effects are not of interest, but are treated more as nuisance variables in an analysis. In this case, the analyst is more interested in the fixed effects and their estimation. This view of random effects characterization is rather narrow because in order to precisely estimate the fixed effects in a model, the random effects have to be properly accounted for. Too few random effects in a model leads to biased estimates of the fixed effects, whereas too many random effects lead to overly large standard errors (Altham, 1984). 

Many times, the density of only one of the random variates in a joint pdf is of interest, while the other variables are considered nuisance variables. The density of one random variable alone is called the marginal pdf and is obtained by integrating out (or averaging out) the... 

Nuisance parameters are generally eliminated by computing the marginal likelihood. In the two-dimensional case, with random variables X and Y, the marginal likelihood can be obtained by integrating out the nuisance parameter from the joint likelihood. For example,... 

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