Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling

ABSTRACT This study was carried out for two purposes: comparing performances of Regression Tree and Automatic Linear Modeling and determining optimum sample size for these methods under different experimental conditions. A comprehensive Monte Carlo Simulation Study was designed for these purposes. Results of simulation study showed that percentage of explained variation estimates of both Regression Tree and Automatic Linear Modeling was influenced by sample size, number of variables, and structure of variance-covariance matrix. Automatic Linear Modeling had higher performance than Regression Tree under all experimental conditions. It was concluded that the Regression Tree required much larger samples to make stable estimates when comparing to Automatic Linear Modeling.

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Optimum Sample Size in Group Comparisons in Animal Breeding Researches with Simulation Study

10.7176/jstr/5-2-33 ◽

2019 ◽

Keyword(s):

Sample Size ◽

Simulation Study ◽

Animal Breeding ◽

Optimum Sample Size ◽

Optimum Sample ◽

Group Comparisons

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The Role of Sample Size in Testing a Hypothesis in Complex Cross-Sectional Studies: A Monte Carlo Simulation Study

Studies in Economics and Econometrics ◽

10.1080/10800379.2008.12106450 ◽

2008 ◽

Vol 32 (2) ◽

pp. 63-68

Author(s):

P A E Serumaga-Zake ◽

R Arnab

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Sample Size ◽

Simulation Study ◽

Cross Sectional ◽

Monte Carlo Simulation Study ◽

Complex Cross ◽

Cross Sectional Studies

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Asymptotically Optimum Sample Size for Quickest Detection

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.1987.313381 ◽

1987 ◽

Vol AES-23 (2) ◽

pp. 263-272 ◽

Cited By ~ 17

Author(s):

L. Pelkowitz ◽

S.C. Schwarts

Keyword(s):

Sample Size ◽

Quickest Detection ◽

Optimum Sample Size ◽

Optimum Sample

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Spatial distribution pattern and sequential sampling plans for Bactrocera oleae (Gmelin) (Dip: Tephritidae) in olive orchards

Journal of Entomological and Acarological Research ◽

10.4081/jear.2016.5533 ◽

2016 ◽

Vol 48 (1) ◽

pp. 23

Author(s):

A. Arbab ◽

F. Mirphakhar

Keyword(s):

Spatial Distribution ◽

Sample Size ◽

Sequential Sampling ◽

Bactrocera Oleae ◽

Sampling Plans ◽

Optimum Sample Size ◽

Precision Level ◽

The Individual ◽

Optimum Sample ◽

Basic Distribution

The distribution of adult and larvae Bactrocera oleae (Diptera: Tephritidae), a key pest of olive, was studied in olive orchards. The first objective was to analyze the dispersion of this insect on olive and the second was to develop sampling plans based on fixed levels of precision for estimating B. oleae populations. The Taylor’s power law and Iwao’s patchiness regression models were used to analyze the data. Our results document that Iwao’s patchiness provided a better description between variance and mean density. Taylor’s b and Iwao’s β were both significantly more than 1, indicating that adults and larvae had aggregated spatial distribution. This result was further supported by the calculated common k of 2.17 and 4.76 for adult and larvae, respectively. Iwao’s a for larvae was significantly less than 0, indicating that the basic distribution component of B. oleae is the individual insect. Optimal sample sizes for fixed precision levels of 0.10 and 0.25 were estimated with Iwao’s patchiness coefficients. The optimum sample size for adult and larvae fluctuated throughout the seasons and depended upon the fly density and desired level of precision. For adult, this generally ranged from 2 to 11 and 7 to 15 traps to achieve precision levels of 0.25 and 0.10, respectively. With respect to optimum sample size, the developed fixed-precision sequential sampling plans was suitable for estimating flies density at a precision level of D=0.25. Sampling plans, presented here, should be a tool for research on pest management decisions of B. oleae.

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