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Relevant bibliographies by topics / Asymmetrical factorial experiments

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Academic literature on the topic 'Asymmetrical factorial experiments'

Author: Grafiati

Published: 8 September 2023

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Journal articles on the topic "Asymmetrical factorial experiments"

1

Gupta, V. K., Rajender Parsad, Lal Mohan Bhar, and Basudev Kole. "Supersaturated Designs for Asymmetrical Factorial Experiments." Journal of Statistical Theory and Practice 2, no. 1 (2008): 95–108. http://dx.doi.org/10.1080/15598608.2008.10411863.

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2

El-Helbawy, Abdalla T., Essam A. Ahmed, and Abdullah H. Alharbey. "Optimal designs for asymmetrical factorial paired comparison experiments." Communications in Statistics - Simulation and Computation 23, no. 3 (1994): 663–81. http://dx.doi.org/10.1080/03610919408813192.

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3

KUMAR, PRAKASH, KRISHAN LAL, ANIRBAN MUKHERJEE, UPENDRA KUMAR PRADHAN, MRINMOY RAY, and OM PRAKASH. "Advanced row-column designs for animal feed experiments." Indian Journal of Animal Sciences 88, no. 4 (2023): 499–503. http://dx.doi.org/10.56093/ijans.v88i4.78895.

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Inappropriate statistical designs may misinterpret results of animal feed experiments. Thus complete statistical designs can make animal feed research more appropriate and cost effective. Usually factorial row-column designs are used when the heterogeneity in the experimental material is in two directions and the experimenter is interested in studying the effect of two or more factors simultaneously. Attempts have been to develop the method of construction of balanced nested row column design under factorial setup. Factorial experiments are used in designs when two or more factors have same le

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4

Agarwal, S. B., and M. N. Das. "Asymmetrical Factorial Type Switch over Designs." Calcutta Statistical Association Bulletin 35, no. 3-4 (1986): 149–56. http://dx.doi.org/10.1177/0008068319860304.

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In the present paper an attempt has been made to construct asymmetrical factorial type switch over designs having strip type arrangement of combination of the levels. To start with two factors at different levels have been considered. One factor has number of levels larger by one compared to the number of levels of other factor. The sequences of levels of second factor are associated with each level symbol of first factor. The situation is similar to strip plot designs in agricultural experiments. Method of construction along with analysis of these designs has been described in this paper.

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5

Divecha, Jyoti, and Bharat Tarapara. "Small, balanced, efficient, optimal, and near rotatable response surface designs for factorial experiments asymmetrical in some quantitative, qualitative factors." Quality Engineering 29, no. 2 (2016): 196–210. http://dx.doi.org/10.1080/08982112.2016.1217338.

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6

Luis Pérez, Carmelo J. "On the Application of a Design of Experiments along with an ANFIS and a Desirability Function to Model Response Variables." Symmetry 13, no. 5 (2021): 897. http://dx.doi.org/10.3390/sym13050897.

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In manufacturing engineering, it is common to use both symmetrical and asymmetrical factorial designs along with regression techniques to model technological response variables, since the in-advance prediction of their behavior is of great importance to determine the levels of variation that lead to optimal response values to be obtained. For this purpose, regression techniques based on the response surface method combined with a desirability function for multi-objective optimization are commonly employed, since it is usual to find manufacturing processes that require simultaneous optimization

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7

Voss, D. T. "On generalizations of the classical method of confounding to asymmetric factorial experiments." Communications in Statistics - Theory and Methods 15, no. 4 (1986): 1299–314. http://dx.doi.org/10.1080/03610928608829183.

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8

Das, Dipa Rani, and Sanjib Ghosh. "An Alternative Method of Construction and Analysis of Asymmetrical Factorial Experiment of the type 6x2² in Blocks of Size 12." Chittagong University Journal of Science 40, no. 1 (2018): 137–50. http://dx.doi.org/10.3329/cujs.v40i1.47920.

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This paper focuses on the construction and analysis of an extra ordinary type of asymmetrical factorial experiment which corresponds to fraction of a symmetrical factorial experiment as indicated by Das (1960). For constructing this design, we have used 3 choices and for each choice we have used 5 different cases. Finding the block contents for each case we have seen that there are mainly two different cases for each choice. In case of analysis of variance, we have seen that, for the case where the highest order interaction effect is confounded in 4 replications, the loss of information is sam

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9

Chatterjee, K., P. Angelopoulos, and C. Koukouvinos. "A lower bound to the measure of optimality for main effect plans in the general asymmetric factorial experiments." Statistics 47, no. 2 (2013): 405–10. http://dx.doi.org/10.1080/02331888.2011.589905.

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10

Rubarth, Kerstin, Paavo Sattler, Hanna Gwendolyn Zimmermann, and Frank Konietschke. "Estimation and Testing of Wilcoxon–Mann–Whitney Effects in Factorial Clustered Data Designs." Symmetry 14, no. 2 (2022): 244. http://dx.doi.org/10.3390/sym14020244.

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Clustered data arise frequently in many practical applications whenever units are repeatedly observed under a certain condition. One typical example for clustered data are animal experiments, where several animals share the same cage and should not be assumed to be completely independent. Standard methods for the analysis of such data are Linear Mixed Models and Generalized Estimating Equations—however, checking their assumptions is not easy, especially in scenarios with small sample sizes, highly skewed, count, and ordinal or binary data. In such situations, Wilcoxon–Mann–Whitney type effects

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