We investigate the difference between regression models in SAS/Stat and compare them to the predictive models in Enterprise Miner. In large samples, the p-value becomes meaningless because the effect size is virtually zero. Therefore, there must be another way to determine the adequacy of the model. In addition, logistic regression cannot be used to predict rare occurrences. Such a model will b...

this study aims at comparing the performance of logistic regression, maximum entropy, and multilayer perceptron techniques in preparing the predictive habitat distribution map of amygdalus scoparia in rangelands of qom province. for this purpose, vegetation sampling was done using random systematic methods after identifying pure habitats of this species. for soil sampling, eight profiles were e...

in the present study, the energetic and economic modeling of lentil and chickpea production in esfahan province of iran was conducted using adaptive neuro-fuzzy inference system (anfis) and linear regression. data were taken by interviewing and visiting of 140 lentil farms and 110 chickpea farms during 2014-2015 production period. the results showed that the yield and total energy consumption w...

Multilevel modeling in general concerns models for relationships between variables defined at different levels of a hierarchical data set, which is often viewed as a multistage sample from a hierarchically structured population. Common applications are individuals within groups, repeated measures within individuals, longitudinal modeling, and cluster randomized trials. This chapter treats the m...

In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and selection of significant variables for the parametric portion. Thus, semiparametric variable selection is much more challenging than parametric variable selection ...

The general principles of Bayesian data analysis imply that models for survey responses should be constructed conditional on all variables that affect the probability of inclusion and nonresponse, which are also the variables used in survey weighting and clustering. However, such models can quickly become very complicated, with potentially thousands of post-stratification cells. It is then a ch...

Capture-recapture models were developed to estimate survival using data arising from marking and monitoring wild animals over time. Variation in survival may be explained by incorporating relevant covariates. We propose nonparametric and semiparametric regression methods for estimating survival in capture-recapture models. A fully Bayesian approach using Markov chain Monte Carlo simulations was...

Super resolution is one of the commonly referred inference problems in computer vision. In the case of images, this problem is generally addressed using a graphical model framework wherein each node represents a portion of the image and the edges between the nodes represent the statistical dependencies. However, the large dimensionality of images along with the large number of possible states f...

Robotic systems are widely applied in process industry to reduce manufacturing labor costs and increase production productivity. Due to the uncertainties existed in the manufacturing environment, the performance improvement of the assembly process is important yet challenging. This paper proposes a regression-based method to predict the performance of the robotic assembly process. Statistical h...

Regression-based approaches are widely used in climate modeling to capture the relationship between a climate variable of interest and a set of predictor variables. These approaches are often designed to minimize the overall prediction errors. However, some climate modeling applications emphasize more on fitting the distribution properties of the observed data. For example, histogram equalizati...