Ratio methods to the mean estimation with known quantiles

2005 ◽  
Vol 170 (2) ◽  
pp. 1031-1044 ◽  
Author(s):  
M. Rueda ◽  
A. Arcos ◽  
S. González-Aguilera ◽  
M.D. Martínez-Miranda ◽  
Y. Román ◽  
...  
Keyword(s):  
Mean Estimation ◽  
The Mean

scholarly journals Estimations of fibre trait stability and type of inheritance in cotton

2018 ◽  
Vol 54 (No. 4) ◽  
pp. 190-192 ◽  
Author(s):  
Vasileios Greveniotis ◽  
Evangelia Sioki ◽  
Constantinos G. Ipsilandis
Keyword(s):  
Stability Index ◽  
Quality Trait ◽  
Quality Traits ◽  
Quantitative Inheritance ◽  
Fibre Quality ◽  
Mean Estimation ◽  
Trait Stability ◽  
Genotype Evaluation ◽  
The Mean ◽  
Almost All

Traits affecting fibre quality were evaluated in a multi-location environmental experiment. Four main cotton regions in Greece were selected as different environments. Five commercial cotton cultivars were used for evaluation of 10 fibre quality traits. Each cultivar was sown in 10 different fields in each region. Environmental fluctuations within regions affected each quality trait differently showing a different degree of inheritance. Four traits showed the lowest stability index values indicating quantitative inheritance, further four traits with intermediate values indicated determination by a few genes, while the more stable and thus with qualitative inheritance traits were considered to indicate fibre maturity and uniformity. The mean estimation of stability in multi-location experiments was found the same as in multi-genotype evaluation. Two cultivars (Elsa and Celia) were found to be more stable across the Greek environments and two regions favoured stability for almost all traits. Correlations between regions were high and the same was found between genotypes.  

scholarly journals Sample size to estimate the mean of morphological traits of rye cultivars in sowing dates and evaluation times

2018 ◽  
Vol 39 (2) ◽  
pp. 521
Author(s):  
Cirineu Tolfo Bandeira ◽  
Alberto Cargnelutti Filho ◽  
Diego Nicolau Follmann ◽  
Cláudia Marques de Bem ◽  
Cleiton Antonio Wartha ◽  
...  
Keyword(s):  
Sample Size ◽  
Plant Height ◽  
Morphological Traits ◽  
Initial Evaluation ◽  
Estimation Errors ◽  
Mean Estimation ◽  
Sowing Dates ◽  
Number Of Leaves ◽  
Size Variability ◽  
The Mean

The objectives of this study were to determine the sample size (number of plants) required to estimate the mean of morphological traits of rye (Secale cereale L.) and verify the sample size variability between the traits, cultivars, sowing dates, and evaluation times for distincts mean estimation errors. Ten uniformity trials were performed with two rye cultivars (BRS Progresso and Temprano) in five sowing dates (05/03/2016, 05/25/2016, 06/07/2016, 06/22/2016 and 07/04/2016). Evaluations of traits plant height, number of leaves, and number of stems were performed during the development of the crop. In order to verify the difference of the traits between cultivars and between sowing dates and evaluation times, the F-test was applied to test the hypothesis of homogeneity of variances and the Student’s t-test was used to test the hypothesis of equality of means. The sample size of each trait was calculated for distincts mean estimation errors. There is sample size variability between the traits, cultivars, sowing dates, and evaluation times. In order to estimate the mean of plant height with the same precision, smaller sample sizes are required at the intermediate and final evaluation times compared to initial evaluation times. For the traits number of leaves and stems, smaller sample sizes are required in the initial evaluation times than in the final evaluation times. For mean estimation of traits with maximum estimation error of 15% between sowing dates and evaluation times, 83 and 103 plants are required respectively for cultivars BRS Progresso and Temprano.

scholarly journals Optimal two-stage sampling for mean estimation in multilevel populations when cluster size is informative

2020 ◽  
pp. 096228022095283
Author(s):  
Francesco Innocenti ◽  
Math JJM Candel ◽  
Frans ES Tan ◽  
Gerard JP van Breukelen
Keyword(s):  
Cluster Size ◽  
Simple Random Sampling ◽  
Equal Probability ◽  
Outcome Variable ◽  
Two Stage ◽  
Mean Estimation ◽  
Informative Cluster Size ◽  
The Mean ◽  
Two Stages ◽  
Number Of Individuals

To estimate the mean of a quantitative variable in a hierarchical population, it is logistically convenient to sample in two stages (two-stage sampling), i.e. selecting first clusters, and then individuals from the sampled clusters. Allowing cluster size to vary in the population and to be related to the mean of the outcome variable of interest (informative cluster size), the following competing sampling designs are considered: sampling clusters with probability proportional to cluster size, and then the same number of individuals per cluster; drawing clusters with equal probability, and then the same percentage of individuals per cluster; and selecting clusters with equal probability, and then the same number of individuals per cluster. For each design, optimal sample sizes are derived under a budget constraint. The three optimal two-stage sampling designs are compared, in terms of efficiency, with each other and with simple random sampling of individuals. Sampling clusters with probability proportional to size is recommended. To overcome the dependency of the optimal design on unknown nuisance parameters, maximin designs are derived. The results are illustrated, assuming probability proportional to size sampling of clusters, with the planning of a hypothetical survey to compare adolescent alcohol consumption between France and Italy.

scholarly journals Nonparametric Mean Estimation for Big-but-Biased Data

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1167
Author(s):  
Laura Borrajo ◽  
Ricardo Cao
Keyword(s):  
Big Data ◽  
Weight Function ◽  
Asymptotic Expression ◽  
Sampling Bias ◽  
Nonparametric Density Estimation ◽  
Nonparametric Method ◽  
Mean Estimation ◽  
Real Population ◽  
The Mean ◽  
Nonparametric Statistical

Some authors have recently warned about the risks of the sentence with enough data, the numbers speak for themselves. The problem of nonparametric statistical inference in big data under the presence of sampling bias is considered in this work. The mean estimation problem is studied in this setup, in a nonparametric framework, when the biasing weight function is unknown (realistic). The problem of ignoring the weight function is remedied by having a small SRS of the real population. This problem is related to nonparametric density estimation. The asymptotic expression for the MSE of the estimator proposed is considered. Some simulations illustrate the performance of the nonparametric method proposed in this work.

scholarly journals On the Mean Estimation using Stratified Double Median Ranked Set Sampling

2017 ◽  
Vol 14 (4) ◽  
pp. 496-502
Author(s):  
Mahmoud Ibrahim Syam ◽  
Amer Ibrahim Al-Omari ◽  
Kamarulzaman Ibrahim
Keyword(s):  
Ranked Set Sampling ◽  
Mean Estimation ◽  
Median Ranked Set Sampling ◽  
The Mean

scholarly journals DamQuick: A New Method for Rapidly Assessing Residual Stand Damage During Partial Timber Harvests

2003 ◽  
Vol 18 (2) ◽  
pp. 81-87
Author(s):  
H-Sup Han ◽  
Thomas W. Steele ◽  
Loren D. Kellogg
Keyword(s):  
General Pattern ◽  
Timber Harvest ◽  
Accurate Method ◽  
Estimation Errors ◽  
Mean Estimation ◽  
Residual Damage ◽  
The Mean ◽  
Stand Damage ◽  
Actual Damage ◽  
Residual Stand Damage

Abstract The increasing frequency of partial timber harvest prescriptions has created the need for an efficient and accurate method of assessing residual stand damage. In this study, we developed and tested a new sampling method that exploits the general pattern of residual stand damage being concentrated near primary transport routes. The method, termed DamQuick, consists of systematic belt transects located immediately adjacent and parallel to the harvesting operation's extraction corridors. Plot measures of tree wounding are scaled by sampling intensity and a concentration factor to yield stand-level estimates of residual damage. The method was evaluated for a skyline thinning operation in western Oregon. DamQuick damage estimates were compared with actual damage levels obtained from a 100% survey for three thinning treatments—30, 60, and 100 residual trees/ac. The mean DamQuick estimates were statistically similar to actual stand damage levels (t = 0.366–0.651, P = 0.531–0.725) for all thinning treatments, with mean estimation errors (standard errors) ranging from 0.7% (0.9%) to 2.1% (4.4%). Operationally, DamQuick was easy to implement. The primary transport routes provided ready stand access, which facilitated plot location and establishment. West. J. Appl. For. 18(2):81–87.

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