Proper Statistical Modeling and Validation in QSAR: A Case Study in the Prediction of Rat Fat-Air Partitioning

2007 ◽  
Author(s):  
Subhash C. Basak ◽  
Denise Mills ◽  
Douglas M. Hawkins ◽  
Jessica J. Kraker ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Statistical Modeling

scholarly journals Statistics in the Service of Science: Don't let the Tail Wag the Dog

2021 ◽  
Author(s):  
Henrik Singmann ◽  
Gregory Edward Cox ◽  
David Kellen ◽  
Suyog Chandramouli ◽  
Clintin Davis-Stober ◽  
...  
Keyword(s):  
Statistical Modeling ◽  
Statistical Models ◽  
Mixed Models ◽  
Model Comparison ◽  
Bayes Factors ◽  
Causal Mechanisms ◽  
Science Models ◽  
Scientific Goal ◽  
Coordination Function

Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, Attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog.These issues are illustrated by van Doorn et al. (in press) in the context of using BayesFactors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination, which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization, which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios.

Statistical modeling the effect of sediment physicochemical properties on the concentration of heavy metal (case study: Musa Creek, SW Iran)

2018 ◽  
Vol 77 (3) ◽  
Author(s):  
Maryam Ghazanfari Shabankareh ◽  
Hakimeh Amanipoor ◽  
Sedigheh Battaleb-Looie ◽  
Javad Dravishi Khatooni
Keyword(s):  
Heavy Metal ◽  
Physicochemical Properties ◽  
Statistical Modeling ◽  
Metal Case ◽  
Sw Iran

scholarly journals Analysis and assessment of productivity in the parquet production by applying the methods of statistical modeling: Case study of selected production system in Serbia

2019 ◽  
pp. 37-56
Author(s):  
Miljan Kalem ◽  
Slobodanka Mitrovic ◽  
Aleksandra Lazarevic
Keyword(s):  
Statistical Modeling ◽  
Production System ◽  
Strong Influence ◽  
Statistical Significance ◽  
Statistical Program ◽  
Main Factors ◽  
The Republic ◽  
Factor Interaction ◽  
Factor Dimension

In this paper, researching results of the selected factors? influence on productivity of parquet production on the example of the selected company in the Republic of Serbia are presented. The analysis has included the influence of the two main factors, dimension and class of parquet quality on productivity. This research has had an aim to determine dependency between the productivity and the mentioned factors, to get corresponding conclusions, to give expert recommendation and to propose suitable management decisions, which would provide an increase in the productivity in the selected company. In order to determine the dependency between the productivity and the mentioned factors, statistical modeling is performed in the program SPSS v.20. Interpretation of the results in the statistical program SPSS, established a strong influence of the analyzed factors on productivity. Statistical significance of the factor dimension influence on productivity is Sig.1= 0.010, while the statistical significance of the parquet class quality factor influence on productivity is Sig. = 0,000. Statistical significance of the factor interaction influence of these factors on productivity is Sig. = 0,028. According to the researching results, it is concluded that parquet productivity depends on the mentioned factors.

As Others See Us: A Case Study in Path Analysis

1987 ◽  
Vol 12 (2) ◽  
pp. 101-128 ◽  
Author(s):  
D. A. Freedman
Keyword(s):  
United States ◽  
Comparative Study ◽  
Path Analysis ◽  
Statistical Modeling ◽  
Social Mobility ◽  
The United States ◽  
Path Model ◽  
Social Data ◽  
Complex Phenomena

In 1967, Blau and Duncan proposed a path model for education and stratification. This is one of the most influential applications of statistical modeling technique to social data. There is recent use of the same technique in Hope’s (1984) comparative study of Scotland and the United States, As Others See Us: Schooling and Social Mobility in Scotland and the United States. A review of path analysis is offered here, with Hope’s model used as an example, the object being to suggest the limits of the method in analyzing complex phenomena.

Operationalizing mirativity

2015 ◽  
Vol 13 (2) ◽  
pp. 353-382 ◽  
Author(s):  
Karolina Krawczak ◽  
Dylan Glynn
Keyword(s):  
Statistical Modeling ◽  
Quantitative Research ◽  
Manual Annotation ◽  
Quantitative Methodology ◽  
Conceptual Category ◽  
Multivariate Statistical ◽  
Multifactorial Analysis ◽  
Usage Dimensions

This study focuses on the conceptual category of mirativity and its constructional construal in English. We propose an operationalization of mirativity with a view to investigating the phenomenon within the usage-based quantitative methodology of multifactorial analysis (Geeraerts, Grondelaers, & Bakema, 1994; Gries, 2003). The proposed operationalization is founded on two usage dimensions, i.e., the degree of performativity of the utterance and the degree of incongruity of the described event. It is argued that mirativity, in its prototypical form, can be operationally defined as a combination of high levels of these two variables. The feasibility of this operationalization in usage-based quantitative research is tested in a case study investigating three surprise-encoding constructions in English: [what + the + np], [what + a + np] and [to + my + np]. The data, amounting to 350 observations of the three constructions, were extracted from dialogic online diaries and submitted to detailed manual annotation and subsequent multivariate statistical modeling. The results reveal a usage continuum ranging from [what + the + np] through [to + my + np] to [what + a + np] relative to the high degrees of performativity and incongruity.

Applying Minimum Night Flow to Estimate Water Loss Using Statistical Modeling: A Case Study in Kinta Valley, Malaysia

2013 ◽  
Vol 27 (5) ◽  
pp. 1439-1455 ◽  
Author(s):  
Jaber M. A. Alkasseh ◽  
Mohd Nordin Adlan ◽  
Ismail Abustan ◽  
Hamidi Abdul Aziz ◽  
Abu Bakar Mohamad Hanif
Keyword(s):  
Statistical Modeling ◽  
Water Loss ◽  
Minimum Night Flow

scholarly journals Video content analysis based on statistical modeling

2021 ◽  
Author(s):  
Jian Zhou
Keyword(s):  
Content Analysis ◽  
Statistical Modeling ◽  
Sequential Data ◽  
Video Content ◽  
Video Content Analysis ◽  
Statistical Measures ◽  
Video Parsing ◽  
Parsing Algorithm ◽  
Semantic Event

This thesis is aimed at finding solutions and statistical modeling techniques to analyze the video content in a way such that intelligent and efficient interaction with video is possible. In our work, we investigate several fundamental tasks for content analysis of video. Specifically, we propose an outline video parsing algorithm using basic statistical measures and an off-line solution using Independent Component Analysis (ICA). A spatiotemporal video similarity model based on dynamic programming is developed. For video object segmentation and tracking, we develop a new method based on probabilistic fuzzy c-means and Gibbs random fields. Theoretically, we develop a generic framework for sequential data analysis. The new framework integrates both Hidden Markov Model and ICA mixture model. The re-estimation formulas for model parameter learning are also derived. As a case study, the new model is applied to golf video for semantic event detection and recognition.

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