Estimation of Prevalence and Relative Risk in Repetitive Surveys

The Theory of double sampling as proposed by Neyman (1938) and subsequently used for successive sampling by Jesson (1942), Yates (1960), Patterson (1950), Eckler (1955), Kuldroff (1963) and Tikkiwal (1960, 1967) has been explored to develop a general estimator which can be used for estimation of parameters such as mean, ratio or double ratio. A simple case of sampling on two occasions has only been considered but the logic can easily be extended for more than two occasions. The results show that the generalised estimator will be very useful for the applied statisticians.     

Median estimation of chemical constituents for sampling on two occasions under a log‐normal model

Sampling from a finite population on multiple occasions introduces dependencies between the successive samples when overlap is designed. Such sampling designs lead to efficient statistical estimates, while they allow estimating changes over time for the targeted outcomes. This makes them very popular in real‐world statistical practice. Sampling with partial replacement can also be very efficient in biological and environmental studies where estimation of toxicants and its trends over time is the main interest. Sampling with partial replacement is designed here on two occasions in order to estimate the median concentration of chemical constituents quantified by means of liquid chromatography coupled with tandem mass spectrometry. Such data represent relative peak areas resulting from the chromatographic analysis. They are therefore positive‐valued and skewed data, and are commonly fitted very well by the log‐normal model. A log‐normal model is assumed here for chemical constituents quantified in mainstream cigarette smoke in a real case study. Combining design‐based and model‐based approaches for statistical inference, we seek for the median estimation of chemical constituents by sampling with partial replacement on two time occasions. We also discuss the limitations of extending the proposed approach to other skewed population models. The latter is investigated by means of a Monte Carlo simulation study.     

Estimation of Partial Regression Coefficient

A sampling scheme providing unbiased partial regression coefficient has been proposed. The proposed sampling scheme is not only unbiased but also superior to simple random sampling and that due to Singh and Bathla (1990) for estimation of partial regression coefficient.     

Estimation of Population Mean Under Successive Sampling Scheme when Various Weights and Regression Coefficients are Unknown

In this paper properties of an estimator of the population mean on current occasion under successive sampling scheme, when various weights (φh'S) and regression coefficients (βh,h-1) are estimated for h ≥ 2, have been studied. Some empirical results on the estimation of the variance of an unbiased estimator of population mean for h = 2 are also given.     

A Note on Successive Sampling Over Two Occasions

In this note, an attempt has been made to study the effect on MVLUE of a population mean on the second occasion in successive sampling when sample values of correlation and regression coefficients are used in it. It was found that increase in the MSE of derived biased estimator is of the order q²ρ² which may be negligible till the proportion of unmatched units (q) and correlation coefficient (ρ) are quite small.     

On a method for estimating the rate of population interchange between two areas

A method for estimating the rate of population interchange between two areas is described in this paper. It is applicable to the case where the sampling ratios and the survival factors of the populations on two areas are different from each other. From the marking-and-recapture data taken at three successive occasions, we can obtain the estimates of emigration factors, total survival factors over two areas and the ratio of surviving migrants to the total survivors over the two areas, for each of two populations initially living on different areas.     

Estimation of Linear Regression Model with Random Coefficients Ensuring Almost Non-Negativity of Variance Estimators

For a linear regression model with random coefficients, this paper considers the estimation of the mean of coefficient vector which, in turn, involves the estimation of variances of random coefficients. The conventional estimation methods for it sometimes provides negative estimates. In order to circumvent this kind of difficulty, a proposal is forwarded and is examined in the light of existing ones.     

Statistical determination of the average values of the extinction coefficients of tryptophan and tyrosine in native proteins.

Spectroscopic measurement of protein concentration requires knowledge of the value of the relevant extinction coefficient. If the amino acid composition of a protein is known, however, extinction coefficients can be calculated approximately, provided that the values of the molar absorptivities for tryptophan and tyrosine residues in the protein are known. We have applied a matrix linear regression procedure and a mapping of average absolute deviations between experimental and calculated values to find molar extinction coefficients (epsilon M, 1 cm, 280 nm) of 5540 M-1 cm-1 for tryptophan and 1480 M-1 cm-1 for tyrosine residues in an "average" protein, as defined by a set of experimentally determined extinction coefficients for more than 30 proteins. Use of these values provides a significant improvement in extinction coefficient estimation over that obtained with the commonly used values obtained from solutions of model compounds in guanidine-HCl. The consistency of these results when compared to the large deviations often observed between experimentally determined extinction coefficients suggest that this method may offer acceptable accuracy in the initial estimation of molar absorptivities of globular proteins.     

从中指骨长度推算身高的研究

作者对近年在华南地区收集的,已知生前身高的汉族成年男性骨骼的中指骨近节、中节进行了测量。用直线回归方程、多元回归方程对从中指骨长度推算身高进行了研究。并用50例国人骨骼标本对这些推算身高的方法作了检验。结果表明,中指骨与四肢大型长骨,以及从中指骨长度推算身高的直线回归方程与多元回归方程,对推算身高的价值都是相同的。     

Estimates of vital rates and predictions of population dynamics change along a long-term monitoring program

Despite the widespread recognition of the importance of monitoring, only a few studies have explored how estimates of vital rates and predictions of population dynamics change with additional data collected along the monitoring program. We investigate how estimates of survival and individual growth, along with predictions about future population size, change with additional years of monitoring and data collected, using as a model system freshwater populations of marble (Salmo marmoratus), rainbow (Oncorhynchus mykiss), and brown trout (Salmo trutta L.) living in Western Slovenian streams. Fish were sampled twice a year between 2004 and 2015. We found that in 3 out of 4 populations, a few years of data (3 or 4 sampling occasions, between 300 and 500 tagged individuals for survival, 100–200 for growth) provided the same estimates of average survival and growth as those obtained with data from more than 15 sampling occasions, while the estimation of the range of survival (i.e., the difference, over all sampling occasions considered, between maximum and minimum survival estimated in a sampling occasion) required more sampling occasions (up to 22 for marble trout), with little reduction of uncertainty around the point estimates. Predictions of mean density and variation in density over time did not change with more data collected after the first 5 years (i.e., 10 sampling occasions) and overall were within 10% of the observed mean and variation in density over the whole monitoring program.     

Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture–recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture–recapture to illustrate volatility across estimation methods.     

ESTIMATING NONLINEAR SELECTION GRADIENTS USING QUADRATIC REGRESSION COEFFICIENTS: DOUBLE OR NOTHING?

The use of regression analysis has been instrumental in allowing evolutionary biologists to estimate the strength and mode of natural selection. Although directional and correlational selection gradients are equal to their corresponding regression coefficients, quadratic regression coefficients must be doubled to estimate stabilizing/disruptive selection gradients. Based on a sample of 33 papers published in Evolution between 2002 and 2007, at least 78% of papers have not doubled quadratic regression coefficients, leading to an appreciable underestimate of the strength of stabilizing and disruptive selection. Proper treatment of quadratic regression coefficients is necessary for estimation of fitness surfaces and contour plots, canonical analysis of the gamma matrix, and modeling the evolution of populations on an adaptive landscape.     

A Dynamic Logistic Regression Model for Multiple Spell Failure Time Data

The paper deals with discrete-time regression models to analyze multistate-multiepisode failure time data. The covariate process may include fixed and external as well as internal time dependent covariates. The effects of the covariates may differ among different kinds of failures and among successive episodes. A dynamic form of the logistic regression model is investigated and maximum likelihood estimation of the regression coefficients is discussed. In the last section we give an application of the model to the analysis of survival time after breast cancer operation.     

Penalized local polynomial regression for spatial data

When performing spatial regression analysis in environmental data applications, spatial heterogeneity in the regression coefficients is often observed. Spatially varying coefficient models, including geographically weighted regression and spline models, are standard tools for quantifying such heterogeneity. In this paper, we propose a spatially varying coefficient model that represents the spatially varying parameters as a mixture of local polynomials at selected locations. The local polynomial parameters have attractive interpretations, indicating various types of spatial heterogeneity. Instead of estimating the spatially varying regression coefficients directly, we develop a penalized least squares regression procedure for the local polynomial parameter estimation, which both shrinks the parameter estimation and penalizes the differences among parameters that are associated with neighboring locations. We develop confidence intervals for the varying regression coefficients and prediction intervals for the response. We apply the proposed method to characterize the spatially varying association between particulate matter concentrations ( ${\text{PM}}_{2.5}$) and pollutant gases related to the secondary aerosol formulation in China. The identified regression coefficients show distinct spatial patterns for nitrogen dioxide, sulfur dioxide, and carbon monoxide during different seasons.     

Two‐stage orthogonality based estimation for semiparametric varying‐coefficient models and its applications in analyzing AIDS data

Semiparametric smoothing methods are usually used to model longitudinal data, and the interest is to improve efficiency for regression coefficients. This paper is concerned with the estimation in semiparametric varying‐coefficient models (SVCMs) for longitudinal data. By the orthogonal projection method, local linear technique, quasi‐score estimation, and quasi‐maximum likelihood estimation, we propose a two‐stage orthogonality‐based method to estimate parameter vector, coefficient function vector, and covariance function. The developed procedures can be implemented separately and the resulting estimators do not affect each other. Under some mild conditions, asymptotic properties of the resulting estimators are established explicitly. In particular, the asymptotic behavior of the estimator of coefficient function vector at the boundaries is examined. Further, the finite sample performance of the proposed procedures is assessed by Monte Carlo simulation experiments. Finally, the proposed methodology is illustrated with an analysis of an acquired immune deficiency syndrome (AIDS) dataset.     

Potential bias in inbreeding depression estimates when using pedigree relationships to assess the degree of homozygosity for loci under selection

A potential bias in estimation of inbreeding depression when using pedigree relationships to assess the degree of homozygosity for loci under selection is indicated. A comparison of inbreeding coefficients based on either pedigree or genotypic frequencies indicated that, as a result of selection, the inbreeding coefficient based on pedigree might not correspond with the random drift of allelic frequencies. Apparent differences in average levels of both inbreeding coefficients were obtained depending on the genetic model (additive versus dominance, initial allelic frequencies, heritability) and the selection system assumed (no versus mass selection). In the absence of selection, allelic frequencies within a small population change over generations due to random drift, and the pedigree-based inbreeding coefficient gives a proper assessment of the accompanying probability of increased homozygosity within a replicate by indicating the variance of allelic frequencies over replicates. With selection, in addition to random drift, directional change in allelic frequencies is not accounted for by the pedigree-based inbreeding coefficient. This result implies that estimation of inbreeding depression for traits under either direct or indirect selection, estimated by a regression of performance on pedigree-based coefficients, should be carefully interpreted.Deceased     

Potential gain from selection for yield stability in two grain sorghum populations

Summary Maximum yield under highly unpredictable environments should be associated with selection of genotypes with superior performance across good and poor environments. Several stability parameters have been proposed to identify superior genotypes over a wide range of environments. None of these has been used as selection criteria, however, because of their low heritability. The objective of the study presented here was to compare the relative efficiency of predicted gain from indirect selection among three stability parameters: the coefficient of regression (b), deviation from regression (S _d ² ), and principal components scores (PC) from the AMMI model; two indices including mean yield and a stability parameter; and three indices involving yield at the best, the worst, and an intermediate environment. Two hundred S₁ families from each of two sorghum populations (TP24D and KP9B) were evaluated at four dry-land evironments over 2 years. The low heritability estimates and the low genetic correlation between the various stability parameters and mean yield resulted in their low relative efficiency as indirect selection criteria for high yield across environments. However, when the parameters were combined with mean yield over all to create indices, the relative efficiency increased for all the environments. In terms of resource allocation, these indices were not as efficient as mean productivity, rank summation, and selection index that involved fewer environments in their estimation.Contribution no. 9820 of Agricultural Research Division, Univ. of Neb. and no. 92-203-J of Kansas Exp. Stn.     

平稳小波变换在冬小麦SPAD高光谱监测中的应用

在2010与2011年度冬小麦生长季通过大田小区试验,利用ASD便携式野外光谱仪和SPAD 502叶绿素计实测冬小麦冠层的高光谱反射率与SPAD值.分析不同SPAD值下的冬小麦冠层光谱特征,建立了基于归一化植被指数（NDVI）与比值植被指数（RVI）、小波能量系数的不同生育期冬小麦SPAD估算模型.结果表明： 随着SPAD值的增大,“绿峰”与“红谷”特征愈加明显.在冬小麦返青期、拔节期、抽穗期、灌浆期NDVI估算SPAD的效果较好,估算模型的R²分别为0.7957、0.8096、0.7557、0.5033.小波能量系数回归模型可以提高冬小麦SPAD的估算精度,在返青期、拔节期、抽穗期、灌浆期以高频、低频小波能量系数为自变量的冬小麦SPAD估算模型的R²分别达到0.9168、0.9154、0.8802、0.9087.     

CLUSTERING POPULATIONS BY MIXED LINEAR MODELS

INTRODUCTIONInevolutionstudiesandplant(oranimal)breedingresearch,clusteranalysesarewidelyusedforgroupingpopulations.Therehavebeenalotofmethodsdevelopedforgroupingpopulations(SneathandSokal,1973;Eve-ritt,1993).Variousmethodsaredifferedinthewaysfordistance(…     

Large-scale seasonal variation in the prevalence and severity of chytridiomycosis

The chytrid fungus Batrachochytrium dendrobatidis has been implicated as the causative agent of mass mortalities, population declines and the extinctions of amphibian species worldwide. Although several studies have shown that the prevalence of chytridiomycosis (the disease caused by the fungus) increases in cooler months, the magnitude and timing of these seasonal fluctuations have yet to be accurately quantified. We conducted disease sampling in a single population of stony creek frogs Litoria wilcoxii on 13 occasions over a 21-month period and used quantitative real-time polymerase chain reaction to detect and quantify the number of B. dendrobatidis zoospores present on samples. Disease prevalence varied significantly across sampling sessions, peaking at 58.3% (in early spring) and dropping to as low as 0% on two occasions (late summer and early autumn). There was a significant negative relationship between disease prevalence and mean air temperature in the 30 days prior to sampling. These large-scale seasonal fluctuations in chytridiomycosis levels will strongly influence conservation programs and amphibian disease research.