Common Departures Research Articles

Least Squares and Robust Rank-Based Double Bootstrap Analyses for Time-Series Intervention Designs.

Time-series intervention designs that include two or more phases have been widely discussed in the healthcare literature for many years. A convenient model for the analysis of these designs has a linear model part (to measure changes in level and trend) plus a second part that measures the random error structure; the error structure is assumed to follow an autoregressive time-series process. Traditional generalized linear model approaches widely used to estimate this model are less than satisfactory because they tend to provide substantially biased intervention tests and confidence intervals. We describe an updated version of the original double bootstrap approach that was developed by McKnight et al. (2000) to correct for this problem. This updated analysis and a new robust version were recently implemented in an R package (McKean & Zhang, 2018). The robust method is insensitive to outliers and problems associated with common departures from normality in the error distribution. Monte Carlo studies as well as published data are used to demonstrate the properties of both versions. The R code required to perform the analyses is provided and illustrated.

Analysis of variance in soil research: Examining the assumptions

Three assumptions underly safe inference from an analysis of variance of soil data from designed experiments and surveys. They are (a) that the residuals are normally distributed, (b) that the within‐groups variances are equal and (c) that effects of treatments and blocks are additive. Small departures from these assumptions are unlikely to affect conclusions because the analysis is robust. Large departures could have serious consequences, however. The most common departures in the real world of the soil are strongly positively skewed distributions of the observed values, many of which are approximately lognormal. In those situations, transformation of the data to logarithms should lead to analyses from which inferences are sound and to confidence limits on estimates. Mean values can be transformed back to the original scales to aid understanding. Measurements of nitrogen in the soil of the Broadbalk wheat experiment show how transformations of the strongly skewed data to logarithms are sufficient to satisfy the assumptions underlying the analysis. Those of nitrogen in the soil of Cashmore Field at Silsoe required the somewhat more complex Box–Cox transformation to satisfy the assumptions and lead to sensible conclusions. The statistical distributions in pH, already a logarithm, in the soil in sandy drift and chalky boulder clay at Broom's Barn Farm differed so strongly that no transformation would satisfy the assumptions. Non‐parametric tests are available, but they should be used only if no suitable transformation can be found.Highlights Safe inference from the analysis of variance is based on three conditions. The conditions are normal distribution of residuals, homogeneity of variances and additivity of effects. Most departures from the conditions in the real world of the soil are caused by positively skewed distributions. Transformation to logarithms will often deal with departures from these conditions.

Analysis of Emergency Medical Services at Pediatrics Department in Trnava University Hospital

The aim of the study is analysis of the average annual percent change indicator (AAPC) of the most common selected ambulance departures causes (%) in the period of six years (2011-2016), divided into two sub-periods (2011-2013 and 2014-2016) at the Pediatrics Department in the Slovak Republic (Trnava University Hospital). The data were collected from the personal health records; where 1,437 the most common causes of ambulance departures were analyzed. In this study was used the trend analysis of average annual percent change (growth/ decline) indicator. In the period of 2011-2016, the highest AAPC of causes of ambulance departures rate was observed due to neuro-circulatory asthenia (AAPC 40.0%; P<0.001) as well as in sub-period 2011-2013 (AAPC 66.7%; P<0.001). In this six-year period, a permanent high decrease in AAPC of ambulance departures causes rate can be observed with lack of appetite. The highest number of ambulance departures was found in injuries, a permanent high increase AAPC of ambulance departures causes rate can be observed in the sub-period 2011-2013 (AAPC 11.8%; P<0.01), and the decrease in sub-period 2014-2016 (AAPC 0.6%). The present analysis of the most common ambulance departures causes could bring benefits regarding the social losses and economic in the Slovak Republic.

An omnibus test of goodness-of-fit for conditional distributions with applications to regression models

We introduce an omnibus goodness-of-fit test for statistical models for the conditional distribution of a random variable. In particular, this test is useful for assessing whether a regression model fits a data set on all its assumptions. The test is based on a generalization of the Cramér–von Mises statistic and involves a local polynomial estimator of the conditional distribution function. First, the uniform almost sure consistency of this estimator is established. Then, the asymptotic distribution of the test statistic is derived under the null hypothesis and under contiguous alternatives. The extension to the case where unknown parameters appear in the model is developed. A simulation study shows that the test has good power against some common departures encountered in regression models. Moreover, its power is comparable to that of other nonparametric tests designed to examine only specific departures.

Goodness-of-fit of conditional regression models for multiple imputation

We propose the calibrated posterior predictive $p$-value ($cppp$) as an interpretable goodness-of-fit (GOF) measure for regression models in sequential regression multiple imputation (SRMI). The $cppp$ is uniformly distributed under the assumed model, while the posterior predictive $p$-value ($ppp$) is not in general and in particular when the percentage of missing data, $pm$, increases. Uniformity of $cppp$ allows the analyst to evaluate properly the evidence against the assumed model. We show the advantages of $cppp$ over $ppp$ in terms of power in detecting common departures from the assumed model and, more importantly, in terms of robustness with respect to $pm$. In the imputation phase, which provides a complete database for general statistical analyses, default and improper priors are usually needed, whereas the $cppp$ requires a proper prior on regression parameters. We avoid this problem by introducing the use of a minimum training sample that turns the improper prior into a proper distribution. The dependency on the training sample is naturally accounted for by changing the training sample at each step of the SRMI. Our results come from theoretical considerations together with simulation studies and an application to a real data set of anthropometric measures.

Effects of nonideal input functions on PET measurements of pulmonary blood flow.

Regional pulmonary blood flow (rPBF) can be measured with an intravenous infusion of 15O-labeled water and positron emission tomography (PET). The current method depends on two assumptions related to the input of activity to the lung during the scan: 1) the pulmonary arterial tracer input is constant (i.e., a "step function" in shape), and 2) the scan begins at the instant of arrival of the step function. To determine the effect that departures from these assumptions might have on the measurement of rPBF, we performed a series of mathematical simulations for three different input functions: 1) a step function that arrived either 1 or 2 s before or after scan start; 2) a dispersed input function, with activity rising during the scan period; and 3) a combination of these two errors. Calculated values, based on the standard assumptions, were compared against the "known" values used in generating the simulated data. The results show that timing errors associated with starting the scan late cause an overestimation of rPBF, whereas timing errors due to low regional flow or departures from the assumed step input function both cause an underestimation of true rPBF. Thus, in actual practice, the combined errors probably partially offset one another. Except for states of truly high rPBF and low lung density, the errors remain less than 15% of the true value. We conclude that PET measurements of rPBF are not highly sensitive to these presumably common departures from the assumed pulmonary arterial input function to lung regions of interest.

Rational Thinking as a Goal of Therapy

To provide a positive goal for cognitive therapies, we define a conception of rational thinking. We describe thinking as a search for possibilities, evidence, and goals, and a process of inference by which the possibilities are evaluated. The possibilities may be choice options, beliefs, or goals. Rational thinking is actively open minded. That is, search is sufficiently thorough, and inference is unbiased in bringing forward the possibility that is best in terms of all the thinker’s goals. The most common departures from rational thinking are insufficient search and bias in favor of possibilities that are already strong. This perspective is applied to the analysis of coping. Cognitive therapies are discussed as training in rational thinking, including instruction in useful heuristics. Suggestions for research are made.

Resistivity cross section through the Juan de Fuca Subduction System and its tectonic implications

A resistivity cross section to depths exceeding 200 km has been derived from magnetotelluric observations along a profile near latitude 45°N from the Juan de Fuca spreading center, across the coastal subduction complex, the High Cascades volcanic arc, and into the back‐arc Deschutes Basin region. In this two‐dimensional interpretation, emphasis was placed on data approximating the transverse magnetic mode since these data are relatively robust to common departures from the two‐dimensional assumption. The vertical magnetic field, however, has been very valuable in defining structure of the offshore sediments, of the oceanic asthenosphere and below the arc volcanics of the Western and High Cascades. The transverse electric data on land suffer a variety of three‐dimensional effects, making their interpretation very difficult. In contrast, the greater uniformity of upper crustal conditions on the seafloor allowed a good fit to both modes of the impedance plus the vertical magnetic field at least down to 104 s. Important components of our model resistivity structure and their hypothesized tectonic implications are as follows. Measurements on the seafloor indicate very low resistivity in the eastward thickening sedimentary wedge of the abyssal Cascadia Basin provided its depth extent is constrained. The majority of these sediments are inferred to be off‐scraped or lose most of their interstitial water before being carried more than ten or so kilometers down the trench. In the oceanic upper mantle of the Juan de Fuca plate, moderately low resistivities from about 35–215 km depth (nominal) presumably reflect up to several percent partial melt attending regional upwelling in the vicinity of the ridge. Very low resistivities below 215 km depth in the oceanic mantle are difficult to explain physically, but problems with very long‐period source field effects or bias error may hamper the modeling here. Under the Oregon Coast Range, a low‐resistivity layer dips inland at approximately 20°. Its conductance decreases by an order of magnitude from the coast to around 60 km inland and its position appears consistent with the Juan de Fuca plate subduction decollement. Possible causes of the low‐resistivity include residual sediments, pore waters, perhaps sulfides, and dehydrating oceanic crust carried down the subduction zone. East of about 60 km inland, there is a much stronger, subhorizontal conductor with a depth to top of around 25 km. To explain it, a massive breakdown of greenschist minerals is hypothesized to liberate most of the water carried in the altered ocean crust well before the volcanic arc is reached. The resistivity manifestation of the arc magmatism itself is not strong but consists of a low‐resistivity axis in the middle crust below and to the west of the arc at the surface. It presumably represents fluids released from crystallizing magmas. East of the High Cascades, both the MT and geomagnetic data detect a strong gradient in resistivity with lower values to the south. It is suggested that the Brothers fault zone signifies an electrical, thus possibly a thermal and magmatic, boundary with the northern Basin and Range.

Ocean Circulation Model Hindcasts of the 1982-83 El Niño: Thermal Variability along the Ship-of-Opportunity Tracks

Abstract Five different analyses of 1982–83 monthly average surface wind stress fields have been used to force an ocean general circulation model of the tropical Pacific, in a series of El Nino hindcast experiments, like the one reported by Philander and Seigel. Although there were prominent common departures from climatology in the surface wind stress field during 1982–83 according to each wind analysis, there are also very substantial differences between analyses. This study was done to investigate the sensitivity of such hindcasts to our uncertain knowledge of the surface wind stress field. We concentrate here on the behavior along the Pacific ship-of-opportunity tracks. According to the ship-of-opportunity XBT data, the ocean underwent major changes during this period. The vertical temperature gradients and mixed layer temperatures, as well as the depth of the thermocline, underwent substantial changes. There were also major changes in the geostrophic flow of the major current systems, as revealed by ...

Thaw–Consolidation of Some Layered Systems

In order to assess the stability and deformation qualities of thawing arctic soils, a theory of thaw–consolidation must be established to predict the dissipation of excess pore fluids. The predictive power of current mathematical models is considerably enhanced by consideration of some common departures from homogeneity. In the first instance a permafrost profile of two different soil types is analyzed numerically, each layer having different thermal and geotechnical properties. The presence of a surficial layer, although minor in extent, may influence for a considerable time the behavior of the underlying layer. Secondly, the excess pore pressures in a fine-grained soil overlying an ice layer are treated theoretically, and the results suggest that foundation conditions over a thawing ice layer may not be as critical as sometimes is supposed.

The k-Visit Method of Consumer Testing

When a pair of products, A and B, is being consumer-tested for preference, it is often found that the results of a single interview are insufficient to determine the attitude of the consumer population with respect to the products. At the interview it is usual to ask the consumer to indicate preference for one of the two products, or to declare no preference. The proportion of no preference votes is particularly dependent on the phrasing of the questionnaire or emphasis of the question. In addition, the information contained in the no preference (or tied) votes has often been wasted, misused, or misinterpreted, in distributing these votes in order to reduce data to two figures: the preferences for A and B. The problem is formulated below as a problem in estimation. A model for the behavior of the consumer population is proposed, and the parameters of the proposed model are estimated. The variancecovariance matrix of the estimators is obtained. A check on the appropriateness of the model is indicated, and some common departures from the model are mentioned. Finally, the method is illustrated with some numerical examples.