s-Level Multiple-Response Main-Effects Factorial Plans

1992 ◽  
Vol 42 (3-4) ◽  
pp. 237-246
Author(s):  
U. Batra ◽  
M.L. Aggarwal
Keyword(s):  
Dispersion Matrix ◽  
Multiple Response ◽  
Relative Importance ◽  
Factorial Experiments ◽  
Response Variable ◽  
Main Effects ◽  
Observation Vector ◽  
Response Variables

This paper deals with construction of plans for s-level factorial experiments in which there are p response variables and each respose is affected by one or more factors. The plans are orthogonal for each response variable. Estimates of the parameters in the models for such plans are obtained when Σ, the dispersion matrix of an observation vector is known. The properties of these estimates can be of help in designing the experiment so that the variances of estimates of the parameters can be influenced by their relative importance.

scholarly journals Joint predictive modeling for geospatial data at various locations

2019 ◽  
Author(s):  
Xi Cheng ◽  
Harry Xie
Keyword(s):  
Predictive Modeling ◽  
Credit Card ◽  
Joint Modeling ◽  
Geospatial Data ◽  
Training Data ◽  
Individual Response ◽  
Multiple Response ◽  
Response Variable ◽  
Auto Correlation ◽  
Response Variables

Predictive modeling uses statistics to predict unknown outcomes. In general, there are two categories of predictive modeling, parametric and non-parametric. There are many applications of predictive modeling, for example, it can be used to predict the risk score of a credit card transaction, it can also be used in health care to identify the probability of having certain disease. When it comes to geospatial data, there are some unique characteristics of the problem. Predictive modeling of geospatial data naturally involves multiple response variables at various locations. The response variables are not independent with each other and thus building separate models for each individual response variable is not appropriate. In addition, many geospatial data has strong spatial auto-correlation such that data from nearby locations are more similar with each other. A joint modeling takes into account of both the correlation among response variables and relationship among different locations, and can make predictions for locations with no training data. In this paper, we review works on joint predictive modeling for multiple response variables at various locations.

Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis

2006 ◽  
Vol 31 (2) ◽  
pp. 157-180 ◽  
Author(s):  
Razia Azen ◽  
David V. Budescu
Keyword(s):  
Multiple Regression ◽  
Multivariate Regression ◽  
Regression Models ◽  
Multiple Response ◽  
Relative Importance ◽  
Multivariate Models ◽  
Minimal Set ◽  
Dominance Analysis ◽  
Response Variables

Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R2 contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria for an appropriate generalization of R2 to the case of multiple response variables. The DA results obtained by univariate regression (with each criterion separately) are analytically compared with results obtained by multivariate DA and illustrated with an example. It is shown that univariate dominance does not necessarily imply multivariate dominance (and vice versa), and it is recommended that researchers who wish to account for the correlation among the response variables use multivariate DA to determine the relative importance of predictors.

scholarly journals Joint predictive modeling for geospatial data at various locations

2019 ◽  
Author(s):  
Xi Cheng ◽  
Harry Xie
Keyword(s):  
Predictive Modeling ◽  
Credit Card ◽  
Joint Modeling ◽  
Geospatial Data ◽  
Training Data ◽  
Individual Response ◽  
Multiple Response ◽  
Response Variable ◽  
Auto Correlation ◽  
Response Variables

Predictive modeling uses statistics to predict unknown outcomes. In general, there are two categories of predictive modeling, parametric and non-parametric. There are many applications of predictive modeling, for example, it can be used to predict the risk score of a credit card transaction, it can also be used in health care to identify the probability of having certain disease. When it comes to geospatial data, there are some unique characteristics of the problem. Predictive modeling of geospatial data naturally involves multiple response variables at various locations. The response variables are not independent with each other and thus building separate models for each individual response variable is not appropriate. In addition, many geospatial data has strong spatial auto-correlation such that data from nearby locations are more similar with each other. A joint modeling takes into account of both the correlation among response variables and relationship among different locations, and can make predictions for locations with no training data. In this paper, we review works on joint predictive modeling for multiple response variables at various locations.

Enhancement of juvenile Caribbean spiny lobsters: an evaluation of changes in multiple response variables with the addition of large artificial shelters

Oecologia ◽  
2006 ◽  
Vol 151 (3) ◽  
pp. 401-416 ◽  
Author(s):  
Patricia Briones-Fourzán ◽  
Enrique Lozano-Álvarez ◽  
Fernando Negrete-Soto ◽  
Cecilia Barradas-Ortiz
Keyword(s):  
Multiple Response ◽  
Spiny Lobsters ◽  
Response Variables

Simultaneous estimation and inference for multiple response variables

2018 ◽  
Vol 48 (11) ◽  
pp. 2734-2747
Author(s):  
Xiaomeng Niu ◽  
Hyunkeun Ryan Cho
Keyword(s):  
Simultaneous Estimation ◽  
Multiple Response ◽  
Response Variables

scholarly journals Tabulation of Multiple Responses

2005 ◽  
Vol 5 (1) ◽  
pp. 92-122 ◽  
Author(s):  
Ben Jann
Keyword(s):  
Data Structure ◽  
Survey Research ◽  
Drug Addiction ◽  
Multiple Response ◽  
Significance Tests ◽  
Multiple Responses ◽  
Interview Question ◽  
Effective Analysis ◽  
Response Variables

Although multiple-response questions are quite common in survey research, Stata's official release does not provide much capability for an effective analysis of multiple-response variables. For example, in a study on drug addiction an interview question might be, “Which substances did you consume during the last four weeks?” The respondents just list all the drugs they took, if any; e.g., an answer could be “cannabis, cocaine, heroin” or “ecstasy, cannabis” or “none”, etc. Usually, the responses to such questions are stored as a set of variables and, therefore, cannot be easily tabulated. I will address this issue here and present a new module to compute one- and two-way tables of multiple responses. The module supports several types of data structure, provides significance tests, and offers various options to control the computation and display of the results. In addition, tools to create graphs of multiple-response distributions are presented.

scholarly journals A Parametric Quantile Regression Model for Asymmetric Response Variables on the Real Line

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1938
Author(s):  
Diego I. Gallardo ◽  
Marcelo Bourguignon ◽  
Christian E. Galarza ◽  
Héctor W. Gómez
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Real Line ◽  
Likelihood Method ◽  
Model Parameters ◽  
Response Variable ◽  
The Real ◽  
Quantile Regression Model ◽  
Asymmetric Response ◽  
Response Variables

In this paper, we introduce a novel parametric quantile regression model for asymmetric response variables, where the response variable follows a power skew-normal distribution. By considering a new convenient parametrization, these distribution results are very useful for modeling different quantiles of a response variable on the real line. The maximum likelihood method is employed to estimate the model parameters. Besides, we present a local influence study under different perturbation settings. Some numerical results of the estimators in finite samples are illustrated. In order to illustrate the potential for practice of our model, we apply it to a real dataset.

Preparation and Evaluation of Zidovudine-Loaded Sustained-Release Microspheres. 2. Optimization of Multiple Response Variables

1996 ◽  
Vol 85 (6) ◽  
pp. 572-576 ◽  
Author(s):  
Khawla Abu-Izza ◽  
Lucila Garcia-Contreras ◽  
D. Robert Lu
Keyword(s):  
Sustained Release ◽  
Multiple Response ◽  
Preparation And Evaluation ◽  
Response Variables ◽  
Sustained Release Microspheres

Statistical power to detect main and interactive effects on the attributes of small-mammal populations

1999 ◽  
Vol 77 (1) ◽  
pp. 68-73 ◽  
Author(s):  
Eric M Schauber ◽  
W Daniel Edge
Keyword(s):  
Statistical Power ◽  
Statistical Tests ◽  
Interactive Effects ◽  
Natural Systems ◽  
Real Effects ◽  
Treatment Variation ◽  
Higher Power ◽  
Using Data ◽  
Main Effects ◽  
Response Variables

Statistical power is an important consideration in the design of experiments, because resources invested in an experiment may be wasted if it is unlikely to produce statistically significant results when real effects or differences exist. Using data from toxicological experiments on seminatural populations of small mammals, we examined the power of statistical tests for main and interactive effects. Our objectives were to evaluate the efficacy of actively reducing within-treatment variation in order to increase power and compare the power provided by several response variables commonly measured in population studies. Controlling population size (N) before treatment increased power to detect effects on N but decreased power to detect effects on population growth (r). For a specified reduction in N, r provided higher power than N. Fractional measures of recruitment generally provided low power, especially when N was low (<20 animals). Power to detect an interaction of two adverse treatments depended on the magnitudes of their main effects, as well as the magnitude of interactive effects. Estimating or predicting effect size is more complex and difficult for interactive effects than for main effects. We conclude that researchers can increase the probability of detecting real effects by choosing response variables with relatively low inherent variability. However, efforts to actively reduce within-treatment variation may have unanticipated repercussions in natural systems.

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