Terms Of Explanatory Variables Research Articles

Assessment of the Functional Form of the Relationship between Balance Control and Physical Activity Regarding Demographic, Anthropometrical, and Eye Impairment Explanatory Covariates in 9- to 11-Year-Old Children: Results of Polynomial and Cluster Analyses.

Explaining the causal and functional relationship between balance control and physical activity (PA) when comparing demographic, anthropometric, and eyesight impairment is uncharted. This study aimed to assess the shape of the relationships between balance control and PA and to verify the usefulness of explanatory variables (sex, chronological age, biological age, myopia, and BMI) in explaining the formation of functional forms between both abovementioned variables. The current contribution evaluated data from 9-11-year-old children (47 boys, 58 girls) and attempts to explain the shape of the relationship between the overall stability index and PA synthetic index, sorting children into clusters depending on their balance features and PA and comparing the separated groups in terms of explanatory variables. The analysis demonstrates four larger clusters that displayed distinct functional relationships. Only BMI and myopia turned out to be useful in explaining cluster memberships. Children in clusters with a linear-constant decline functional form were mostly non-myopic girls, thin, and less mature. Meanwhile, it becomes clear that children with an L-shape were myopic, early-maturated boys with a BMI in the middle of the range for normal weight. The pattern of an inverted U-shaped functional form was distinctive for myopes with rather high BMIs compared to their peers but normally matured.

A useful semiparametric regression for climatology

In climatology, there are several situations for which the response variable has linear and non-linear relations simultaneously in terms of explanatory variables. Another common situation in this area is when the distribution of the response variable presents a bimodal form. So, it is not possible analyzing this type of data using classical distributions belonging to the symmetric class (normal, t-Student, exponential power, etc.), or distributions in an asymmetric class (Weibull, log-logistic, skew normal, etc.). For these problems, we propose a semiparametric regression using a flexible distribution, named the odd log-logistic exponential Gaussian, whose systematic component contains parametric and nonparametric parts to measure the linear and non-linear effects simultaneously from a penalized smoothing based on the P-spline. We add new structural properties of the proposed distribution. Adopting penalized maximum likelihood to estimate the parameters and some simulations show the accuracy of the estimators. Diagnostic measures and residual analysis are discussed. We prove the versatility of our proposal in the climatology area.

Spatio-temporal variations of nitric acid total columns from 9 years of IASI measurements – a driver study

Abstract. This study aims to understand the spatial and temporal variability of HNO3 total columns in terms of explanatory variables. To achieve this, multiple linear regressions are used to fit satellite-derived time series of HNO3 daily averaged total columns. First, an analysis of the IASI 9-year time series (2008–2016) is conducted based on various equivalent latitude bands. The strong and systematic denitrification of the southern polar stratosphere is observed very clearly. It is also possible to distinguish, within the polar vortex, three regions which are differently affected by the denitrification. Three exceptional denitrification episodes in 2011, 2014 and 2016 are also observed in the Northern Hemisphere, due to unusually low arctic temperatures. The time series are then fitted by multivariate regressions to identify what variables are responsible for HNO3 variability in global distributions and time series, and to quantify their respective influence. Out of an ensemble of proxies (annual cycle, solar flux, quasi-biennial oscillation, multivariate ENSO index, Arctic and Antarctic oscillations and volume of polar stratospheric clouds), only the those defined as significant (p value < 0.05) by a selection algorithm are retained for each equivalent latitude band. Overall, the regression gives a good representation of HNO3 variability, with especially good results at high latitudes (60–80 % of the observed variability explained by the model). The regressions show the dominance of annual variability in all latitudinal bands, which is related to specific chemistry and dynamics depending on the latitudes. We find that the polar stratospheric clouds (PSCs) also have a major influence in the polar regions, and that their inclusion in the model improves the correlation coefficients and the residuals. However, there is still a relatively large portion of HNO3 variability that remains unexplained by the model, especially in the intertropical regions, where factors not included in the regression model (such as vegetation fires or lightning) may be at play.

Bayesian Non-Parametric Models for Spatially Indexed Data of Mixed Type

SummaryWe develop Bayesian non-parametric models for spatially indexed data of mixed type. Our work is motivated by challenges that occur in environmental epidemiology, where the usual presence of several confounding variables that exhibit complex interactions and high correlations makes it difficult to estimate and understand the effects of risk factors on health outcomes of interest. The modelling approach that we adopt assumes that responses and confounding variables are manifestations of continuous latent variables and uses multivariate Gaussian distributions to model these jointly. Responses and confounding variables are not treated equally as relevant parameters of the distributions of the responses only are modelled in terms of explanatory variables or risk factors. Spatial dependence is introduced by allowing the weights of the non-parametric process priors to be location specific, obtained as probit transformations of Gaussian Markov random fields. Confounding variables and spatial configuration have a similar role in the model, in that they influence, along with the responses, only the allocation probabilities of the areas into the mixture components, thereby allowing for flexible adjustment of the effects of observed confounders, while allowing for the possibility of residual spatial structure, possibly occurring because of unmeasured or undiscovered spatially varying factors. Aspects of the model are illustrated in simulation studies and an application to a real data set.

Bayesian mixed-effects location and scale models for multivariate longitudinal outcomes: an application to ecological momentary assessment data.

In the statistical literature, the methods to understand the relationship of explanatory variables on each individual outcome variable are well developed and widely applied. However, in most health-related studies given the technological advancement and sophisticated methods of obtaining and storing data, a need to perform joint analysis of multivariate outcomes while explaining the impact of predictors simultaneously and accounting for all the correlations is in high demand. In this manuscript, we propose a generalized approach within a Bayesian framework that models the changes in the variation in terms of explanatory variables and captures the correlations between the multivariate continuous outcomes by the inclusion of random effects at both the location and scale levels. We describe the use of a spherical transformation for the correlations between the random location and scale effects in order to apply separation strategy for prior elicitation while ensuring positive semi-definiteness of the covariance matrix. We present the details of our approach using an example from an ecological momentary assessment study on adolescents.

Global environment- and space-richness ranking relationships: The effects of interaction and high-order terms of explanatory variables

In the present study, the interplay and higher-order terms of environmental and spatial variables are considered to evaluate the relations of environment and space-species richness rankings at global scale. Three taxonomic groups composed of mammals, birds and amphibians were analyzed for the study. Thek-means clustering method was introduced for richness rankings detection and analysis from published digital maps; and simple regression analysis and AIC criteria were used for identifying mostimportant correlated explanatory variables.When comparing each single variable, I found that latitude was the most important one influencing global vertebrate richness rankings. When onlyconsidering environmental variables, I foundthat precipitation was the only predictor of vertebrate richness rankings. However, when the interaction and high-order terms of different independent variables were considered, it was found that the interaction between latitude and temperature could better explain the global bird richness ranking, while the second-power effectof latitude was the best predictor for amphibianand mammalian richness rankings, as evidenced by the AIC model selection and comparison among the regression models. In conclusion, the inclusion of high-order and interaction terms of environmental and spatial variables could offer more insights into the understanding of global species diversity patterns.

Neural networks for regional employment forecasts: are the parameters relevant?

In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models—in terms of explanatory variables and NN structures—we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models.

Modelling airport and airline choice behaviour with the use of stated preference survey data

The majority of studies of air travel choice behavior make use of revealed preference (RP) data, generally in the form of survey data collected from departing passengers. While the use of RP data has certain methodological advantages over the use of stated preference (SP) data, major issues arise because of the often low quality of the data relating to the un-chosen alternatives, in terms of explanatory variables as well as availability. As such, studies using RP survey data often fail to recover a meaningful fare coefficient, and are generally not able to offer a treatment of the effects of airline allegiance. In this paper, we make use of SP data for airport and airline choice collected in the US in 2001. The analysis retrieves significant effects relating to factors such as airfare, access time, flight time and airline and airport allegiance, illustrating the advantages of SP data in this context. Additionally, the analysis explores the use of non-linear transforms of the explanatory variables, as well as the treatment of continuous variations in choice behavior across respondents.

Explaining a presence of groups in analytical data in terms of original variables

This manuscript shows the usefulness of Projection Pursuit (PP) and Multivariate Regression Trees (MRT) for analytical data exploration. Additionally, features of Projection Pursuit and kurtosis as a projection index are presented. The ability of Projection Pursuit to discover groups in the data is compared to classical Principal Component Analysis (PCA). Moreover, it is also demonstrated how the presence of groups in the data can be explained in terms of explanatory variables with the aid of Projection Pursuit and Multivariate Regression Trees. Neither Projection Pursuit nor Multivariate Regression Trees are commonly used for exploring chemical data however, they are able to enrich to a high extent the interpretation.

Selective Decisions: Challenging Orthodox Analyses of the Hiring Process

Employee recruitment and selection decisions are increasingly framed in terms of explanatory variables and interactions that span multiple levels of analysis. This paper reviews the gradual transition in thinking from predominantly micro‐level explanations, to frameworks more accepting of the importance of both micro‐ and macro‐level explanations. The latter accommodate the influences of and interactions between, for example, the individual characteristics of candidates; the cognitive processes of key actors or decision‐makers; organisational characteristics; collective processes; and wider environmental contextual factors which constrain and shape an organisation’s or manager’s decision alternatives. The result is more complex cross‐level theorising about the hiring process. The paper also goes beyond cross‐level interactions between multiple variables in considering selection as a social relational phenomenon. Meso‐level theories present epistemological alternatives to the dominant micro‐level research tradition in selection and potentially more parsimonious and powerful micro–macro bridging concepts than cross‐level interactionist perspectives. It is argued that cross‐disciplinary perspectives, although still problematic, are valuable additions to the individualistic paradigm which is conventionally used to understand employee recruitment and selection.

The link between tax rates and foreign direct investment

This paper investigates the impact of tax policy on foreign direct investment flows between the US and other countries using a panel data empirical approach. Using panel data is an attractive alternative to using single time series data because it provides greater statistical power and offers greater flexibility in terms of explanatory variables. This study finds many significant factors influencing the transfer of funds component of foreign direct investment. Most noteworthy is that, in addition to host and home country corporate tax rates having a significant effect on investment flows, the host and home country income tax rates are also significant.

A Methodology for Surveying Domestic Water Consumption

AbstractThis paper describes the experimental design and engineering of the largest contemporary survey of domestic water consumption in the UK. Results are presented for the first full year of data collection and are discussed in terms of explanatory variables, reliability and usefulness. Significant findings include (a) the range of per capita consumption, (b) relationships to explanatory variables, (c) components of consumption including legitimate night flows, and (d) the contributions of specific domestic appliances.

The Use of Logistic Models for the Analysis of Codon Frequencies of DNA Sequences in Terms of Explanatory Variables

The development of the regressive logistic model applicable to the analysis of codon frequencies of DNA sequences in terms of explanatory variables is presented. A codon is a triplet of nucleotides that code for an amino acid, and may be considered as a trivariate response (B1, B2, B3), where Bi (i = 1, 2, 3) is a categorical random variable with values A, C, G, T. The linear order of bases in the DNA and possible statistical dependence of the bases in a given codon make the regressive logistic model a suitable tool for the analysis of codon frequencies. A problem of structural zeros arises from the fact that the stopping codons (terminators) do not code for amino acids; this is solved by normalizing the likelihood function. Codon frequencies may also depend on the function of the gene and they are known to differ between genes of the same genome. Differences also occur between synonymous codons for the same amino acid. Thus, the use of covariates that differ between synonymous codons as well as covariates that are constant within codons of the same amino acid may be useful in explaining the frequencies. As an illustration, the method is applied to the human mitochondrial genome using the following as explanatory variables: (1) TSCORE, a measure of the number of single base mutations required for a given codon to become a terminator; (2) AARISK, an indicator of a codon's ability of changing by a single base substitution to triplets coding for amino acids with very different characteristics; (3) AVDIST, a measure of the typicality of the amino acid coded for by the triplets. The results indicate that models that incorporate dependency structure and covariates are to be preferred to either the models comprising covariates alone or dependency structure alone.

Rural-destined migration in Third World settings: a neglected phenomenon?

This paper 1st reviews earlier research on rural-destined migration in Third World settings, in order to derive generalizations for a topic most often treated in idiosyncratic fashion. Descriptive analysis of all Costa Rican migration flows for 1968-73 then establishes that a surprisingly high proportion are rural directed. Finally, statistical analyses identify differences, in terms of explanatory variables, between urban-and rural-destined migrations, and for rural-destined migrations only, between those originating in urban and rural areas.

An Extension of the Proportional-Hazards Model for Grouped Data

A family of transformations for probabilities is considered for the analysis of grouped survival data. Additive and multiplicative models for the hazard function are compared. A method is suggested for estimating the scale on which an additive representation of the hazard, in terms of explanatory variables, is appropriate. Computations are performed using GLIM. A test for departures from the grouped proportional-hazards model is introduced. An example is analyzed by the methods put forward.