Multifactor Experiments

Multifactor experiments investigate the impact of two or more factors or input parameters on a process' output response. Factorial experiment design, or simply factorial design, is a systematic approach for articulating the procedures required to successfully run a factorial experiment. Estimating the effects of numerous parameters on a process' output with a small number of observations is crucial for process output optimization

The Development Of Optimization Methods For Knowledge Base Enrichment Processes

The paper presents the concept of approach to the research and evaluation of the processes of intellectual activity associated with the enrichment of the knowledge base. A feature of the research of the process dynamics is the need of simultaneous consideration of such diverse factors as the complexity of information perception, the presence of the deviations of the response from the standard in the process of reproduction and accounting of the test time.A significant influence on the methods of optimization of the knowledge base enrichment process is exerted by a considerable duration of the task learning process. This causes the use of the multifactor experimental design theory to accelerate the progress towards the optimum.The research results can be used in the development of technologies for efficient knowledge assimilation, automation of skills, and also in the development of expert systems for diagnostics of the processes of intellectual activity

Systematic and multifactor risk models revisited

Systematic and multifactor risk models are revisited via methods which were already successfully developed in signal processing and in automatic control. The results, which bypass the usual criticisms on those risk modeling, are illustrated by several successful computer experiments.Comment: First Paris Financial Management Conference, Paris : France (2013

Large Panels with Common Factors and Spatial Correlations

This paper considers the statistical analysis of large panel data sets where even after conditioning on common observed effects the cross section units might remain dependently distributed. This could arise when the cross section units are subject to unobserved common effects and/or if there are spill over effects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the concepts of time-specific weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight matrix

Experimentation in Psychology--Rationale, Concepts and Issues

An experiment is made up of two or more data-collection conditons that are identical in all aspects, but one. It owes its design to an inductive principle and its hypothesis to deductive logic. It is the most suited for corroborating explanatory theries , ascertaining functional relationship, or assessing the substantive effectiveness of a manipulation. Also discussed are (a) the three meanings of 'control,' (b) the issue of ecological validity, (c) the distinction between theory-corroboration and agricultural-model experiments, and (d) the distinction among the hypotheses at four levels of abstraction that are implicit in an experiment

iSeqQC: a tool for expression-based quality control in RNA sequencing.

BACKGROUND: Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise an experiment and the resulting conclusions. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers. RESULTS: Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers. It can be utilized through command-line (Github: https://github.com/gkumar09/iSeqQC) or web-interface (http://cancerwebpa.jefferson.edu/iSeqQC). A local shiny installation can also be obtained from github (https://github.com/gkumar09/iSeqQC). CONCLUSION: iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches