Precision and accuracy of sampling methods in thinned Pinus taeda L. stands

Different sampling methods can be used in forest surveys. It is important to know the precision and accuracy of these sampling methods, and which one is the most appropriate in specific conditions of the forest population. The aim of this study was to compare estimates of a forest inventory performed by different sampling methods with forest census results. The sampling methods evaluated were the fixed-area method and the variable-area methods of Bitterlich, Prodan, and Strand. The data were obtained in a 15-year-old thinned stand of Pinus taeda L., located in the municipality of Teixeira Soares, southern Brazil, with a total area of 12.80 ha. Initially, the forest census was carried out, and subsequently, the sample units for each sampling method were distributed in the stand, with a common starting point. The variables used to compare the sample results with the census means were quadratic diameter, number of trees, basal area, and volume, per hectare. Precision and accuracy were evaluated by sampling error and whether the confidence intervals covered the population means, respectively. The fixed-area and Bitterlich methods stood out in precision for all variables analysed. The fixed-area, Bitterlich and Strand methods with proportion to height provided more accurate estimates. The Prodan method provided inaccurate and imprecise estimates for the variables under analysis, except for the quadratic diameter.

On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design

In this chapter, the authors consider the problem of estimating the population means of two sensitive variables by making use ranked set sampling. The final estimators are unbiased and the variance expressions that they derive show that ranked set sampling is more efficient than simple random sampling. A convex combination of the variance expressions of the resultant estimators is minimized in order to suggest optimal sample sizes for both sampling schemes. The relative efficiency of the proposed estimators is then compared to the corresponding estimators for simple random sampling based on simulation study and real data applications. SAS codes utilized in the simulation to collect the empirical evidence and application are included.

On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design

In the chapter, the authors consider the problem of estimating the population means of two sensitive variables by making use of ranked set sampling. The final estimators are unbiased and the variance expressions that they derive show that ranked set sampling is more efficient than simple random sampling. A convex combination of the variance expressions of the resultant estimators is minimized in order to suggest optimal sample sizes for both sampling schemes. The relative efficiency of the proposed estimators is then compared to the corresponding estimators for simple random sampling based on simulation study and real data applications. SAS codes utilized in the simulation to collect the empirical evidence and application are included.

Generalization of the order-restricted information criterion for multivariate normal linear models (pre-print )

The Akaike information criterion for model selection presupposes that the parameter space is not subject to order restrictions or inequality constraints.Anraku (1999) proposed a modified version of this criterion, called the order-restricted information criterion, for model selection in the one-way analysis of variance model when the population means are monotonic.We propose a generalization of this to the case when the population means may be restricted by a mixture of linear equality and inequality constraints.If the model has no inequality constraints, then the generalized order-restricted information criterion coincides with the Akaike information criterion.Thus, the former extends the applicability of the latter to model selection in multi-way analysis of variance models when some models may have inequality constraints while others may not. Simulation shows that the information criterion proposed in this paper performs well in selecting the correct model.

An Akaike-type information criterion for model selection under inequality constraints (pre-print)

The Akaike information criterion for model selection presupposes that the parameter space is not subject to order restrictions or inequality constraints. Anraku (1999) proposed a modified version of this criterion, called the order-restricted information criterion, for model selection in the one-way analysis of variance model when the population means are monotonic. We propose a generalization of this to the case when the population means may be restricted by a mixture of linear equality and inequality constraints. If the model has no inequality constraints, then the generalized order-restricted information criterion coincides with the Akaike information criterion. Thus, the former extends the applicability of the latter to model selection in multi-way analysis of variance models when some models may have inequality constraints while others may not. Simulation shows that the information criterion proposed in this paper performs well in selecting the correct model.

Smartphone Addiction in Mexican Engineering Students

Students academic performance could be affected by excessive use of the smartphone. This study focuses on analyzing the level of cell phone addiction in engineering university students. It also seeks to determine if there is a difference by gender in this behavior. 306 engineering students from a Technological Institute in Veracruz, Mexico participated in the study. The instrument used to obtain data was SAS-SV (Smartphone addiction scale-short version) designed by [1]. In order to identify the set of indicators with the highest factor loadings, an exploratory factor analysis was carried out with extraction of components and orthogonal rotation with the Varimax method. To identify if there is a difference by gender, the t test is used to contrast the hypothesis about two independent population means. The findings demonstrated the extraction of two components, which differs from the one-dimensional model proposed by [1], and no difference was found between the groups of male and female students.

Integration of Nonprobability and Probability Samples via Survey Weights

Probability sample encounters the problems of increasing cost and nonresponse. The cost has rapidly been increasing in executing a large probability sample survey, and, for some surveys, response rate can be below the 10 percent level. Therefore, statisticians seek some alternative methods. One of them is to use a large nonprobability sample (S_1 ) supplemented by a small probability sample (S_2 ). Both samples are taken from the same population and they include common covariates, and a third sample (S_3 ) is created by combining these two samples; S_1  can be biased and S_2  may have large sample variance. These two problems are reduced by survey weights and combining the two samples. Although S_2  is a small sample, it provides good properties of unbiasedness in estimation and of survey weights. With these known weights, we obtain adjusted sample weights (ASW), and create a sample model from a finite population model. We fit the sample model to obtain its parameters and generate values from the population model. Similarly, we repeat these processes for other two samples, S_1  and S_3  and for different statistical methods. We show reduced biases of the finite population means and reduced variances.as the combined sample size becomes large. We analyze sample data to show the reduction of these two errors.

Association Between Backfat Thickness at 28 Weeks Old and Production Traits of Primiparous Landrace Sows Raised Under Tropical Conditions

Backfat thickness could reflex energy reserve of the female pigs that is required for their productivity, especially gilts that might be selected for the replacements. Therefore, phenotypic and genetic correlations between backfat thickness (BF) and production traits were estimated and considered for the possibility of using BF at pre-selective stage as an early indicator for productivity of the sow. Pedigree information, BF and body weight (BW) at 28 weeks old, age at first farrowing (AFF), transformed proportion of piglet loss at birth (tPL) and transformed weaning to first service interval (tWSI) of 806 primiparous Landrace sows were used to estimate the variance components by restricted maximum likelihood procedure with an average information algorithm for multivariate analysis. Genetic correlation between BF and BW was 0.70 ± 0.13. Both BF and BW had negative genetic correlation with AFF, but not tWSI. Unfortunately, genetic correlation estimates between tPL and other traits was unclear due to high standard error. The genetic correlation between AFF and tWSI was 0.78 ± 0.36. Besides 19.35% of sires, 26.34% of dams and 25.81% of sows had genetic ability for BF, BW, AFF and WSI above the population means. The genetic association between BF, BW, AFF and tWSI indicated the feasibility of using selection index to improve BF and BW at pre-selective stage and reduce AFF and tWSI of replacement gilt simultaneously. However, the estimation of genetic correlation between PL and other traits should be repeated in a larger population.

Dietary Intake by Urbanization Level Among U.S. Youth Ages 2 -19 Years, NHANES 2013–2018

Objectives To describe differences in dietary intake of select foods and nutrients by level of urbanization in U.S. youth ages 2–19 years. Methods Data from 8301 youth in the National Health and Nutrition Examination Survey, 2013–2018 were used. Mean ratios were estimated for % of fruit consumed as whole fruit (WF) and grains as whole grains (WG), % of energy from added sugars (AS) and saturated fats (SF), and mean intake of fiber, potassium, and sodium on a given day. Proportions consuming >50% (fruit as WF and grains as WG), <10% of energy (AS and SF), and population means for fiber, potassium, and sodium, compared to the age and sex-specific daily nutritional goals, were also estimated. Trends across urbanization [large metropolitan statistical areas (MSAs), medium or small MSAs, and non-MSAs], by sex, and by income (lower income: <130% Federal poverty level (FPL) and higher income: ≥130% FPL) were examined using orthogonal polynomials with P < 0.05 significance level. Results Adjusted for age, on a given day, the percentage who consumed >50% of fruit as WF increased significantly from 43.7% in non-MSAs to 51.0% in large MSAs, and a significant increase was seen among males. The % of energy from AS decreased significantly from 15.5% in non-MSAs to 13.2% in large MSAs, and significant declines were also seen among males and among lower income youth, and the percentage of males who consumed <10% of energy from AS also increased significantly from 30.5% in non-MSAs to 40.5% in large MSAs. Fiber intake increased significantly from 12.9 g in non-MSAs to 14.5 g in large MSAs, and significant increases in intake were seen among males and higher income youth. More than 10% (10.5%) of youth in non-MSAs and 14.8% of youth in large MSAs were at or above the daily nutritional goals for fiber. Overall, there were no trends by urbanization for WG, SF, potassium, and sodium. Conclusions These findings provide insight into dietary intake of US youth by level of urbanization, and sociodemographic factors that may influence intake, including sex and income. Overall, with increasing urbanization level, a higher proportion of youth consumed >50% of fruit as WF, <10% of energy from AS, and more fiber. Funding Sources N/A.