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

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.

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 A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Ali Salmasnia ◽  
Mahdi Bastan ◽  
Asghar Moeini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Multiple Response ◽  
Process Variables ◽  
Multiple Response Optimization ◽  
Response Optimization ◽  
Artificial Neural ◽  
The Relationship ◽  
Response Variables

An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables), called a multiple response optimization (MRO) problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM), due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.

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