The crustal movements of the Chinese mainland include an average regional movement trend of the mainland and complex local deformations. Thus, both trends in the crustal movement of the mainland and local distortions ...The crustal movements of the Chinese mainland include an average regional movement trend of the mainland and complex local deformations. Thus, both trends in the crustal movement of the mainland and local distortions should be simultaneously taken into consideration in crustal movement estimations. A combined collocation model based on Euler vector (taken as trend parameters) and local distortions (taken as signals) is proposed in this paper. We assume that prior covariance matrices between signals and observations should be consistent with their uncertainties. Otherwise, the station movement estimates provided by the collocation will be distorted. Thus, an adaptive collocation estimator based on simplified Helmert variance components is applied. This means that the contributions of signals and observations to estimates of crustal movements are balanced and reasonable, and consistent covariance matrices of the signals and observations are achieved through the adjustment of the adaptive factor. The calculation of actual horizontal movements of the Chinese crust shows that the estimates of horizontal crustal movement velocities are made more accurate by the adaptive collocation model.展开更多
In linear mixed models, there are two kinds of unknown parameters: one is the fixed effect, theother is the variance component. In this paper, new estimates of these parameters, called the spectral decom-position esti...In linear mixed models, there are two kinds of unknown parameters: one is the fixed effect, theother is the variance component. In this paper, new estimates of these parameters, called the spectral decom-position estimates, are proposed, Some important statistical properties of the new estimates are established,in particular the linearity of the estimates of the fixed effects with many statistical optimalities. A new methodis applied to two important models which are used in economics, finance, and mechanical fields. All estimatesobtained have good statistical and practical meaning.展开更多
For a general linear mixed model with two variance components, a set of simple conditions is obtained, under which, (i) the least squares estimate of the fixed effects and the analysis of variance (ANOVA) estimates of...For a general linear mixed model with two variance components, a set of simple conditions is obtained, under which, (i) the least squares estimate of the fixed effects and the analysis of variance (ANOVA) estimates of variance components are proved to be uniformly minimum variance unbiased estimates simultaneously; (ii) the exact confidence intervals of the fixed effects and uniformly optimal unbiased tests on variance components are given; (iii) the exact probability expression of ANOVA estimates of variance components taking negative value is obtained.展开更多
Various random models with balanced data that are relevant for analyzing practical test data are described, along with several hypothesis testing and interval estimation problems concerning variance components. In thi...Various random models with balanced data that are relevant for analyzing practical test data are described, along with several hypothesis testing and interval estimation problems concerning variance components. In this paper, we mainly consider these problems in general random effect model with balanced data. Exact tests and confidence intervals for a single variance component corresponding to random effect are developed by using generalized p-values and generalized confidence intervals. The resulting procedures are easy to compute and are applicable to small samples. Exact tests and confidence intervals are also established for comparing the random-effects variance components and the sum of random-effects variance components in two independent general random effect models with balanced data. Furthermore, we investigate the statistical properties of the resulting tests. Finally, some simulation results on the type Ⅰ error probability and power of the proposed test are reported. The simulation results indicate that exact test is extremely satisfactory for controlling type Ⅰ error probability.展开更多
Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are imperfect.To address these...Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are imperfect.To address these issues,the mixed model proposed here first estimates the genotypic effects for AA,Aa,and aa,and the genotypic polygenic background replaces additive and dominance polygenic backgrounds.Then,the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model.This strategy was further expanded to cover QTN-by-environment interactions(QEIs)and QTN-by-QTN interactions(QQIs)using the same mixed-model framework.Thus,a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model(mrMLM)method to establish a new methodological framework,3VmrMLM,that detects all types of loci and estimates their effects.In Monte Carlo studies,3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects,with high powers and accuracies and a low false positive rate.In re-analyses of 10 traits in 1439 rice hybrids,detection of 269 known genes,45 known gene-by-environment interactions,and 20 known gene-by-gene interactions strongly validated 3VmrMLM.Further analyses of known genes showed more small(67.49%),minor-allele-frequency(35.52%),and pleiotropic(30.54%)genes,with higher repeatability across datasets(54.36%)and more dominance loci.In addition,a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs,and variable selection under a polygenic background was proposed for QQI detection.This study provides a new approach for revealing the genetic architecture of quantitative traits.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos. 41020144004 and 41004013)
文摘The crustal movements of the Chinese mainland include an average regional movement trend of the mainland and complex local deformations. Thus, both trends in the crustal movement of the mainland and local distortions should be simultaneously taken into consideration in crustal movement estimations. A combined collocation model based on Euler vector (taken as trend parameters) and local distortions (taken as signals) is proposed in this paper. We assume that prior covariance matrices between signals and observations should be consistent with their uncertainties. Otherwise, the station movement estimates provided by the collocation will be distorted. Thus, an adaptive collocation estimator based on simplified Helmert variance components is applied. This means that the contributions of signals and observations to estimates of crustal movements are balanced and reasonable, and consistent covariance matrices of the signals and observations are achieved through the adjustment of the adaptive factor. The calculation of actual horizontal movements of the Chinese crust shows that the estimates of horizontal crustal movement velocities are made more accurate by the adaptive collocation model.
基金This work was supported by the National Natural Science Foundation of China, the Natural Science Foundation of Beijing, and a project of Science and Technology of Beijing Education Committee.
文摘In linear mixed models, there are two kinds of unknown parameters: one is the fixed effect, theother is the variance component. In this paper, new estimates of these parameters, called the spectral decom-position estimates, are proposed, Some important statistical properties of the new estimates are established,in particular the linearity of the estimates of the fixed effects with many statistical optimalities. A new methodis applied to two important models which are used in economics, finance, and mechanical fields. All estimatesobtained have good statistical and practical meaning.
基金This work was partially supported by the National Natural Science Foundation of China(Grant No.10271010)the Natural Science Foundation of Beijing(Grant Mo.1032001).
文摘For a general linear mixed model with two variance components, a set of simple conditions is obtained, under which, (i) the least squares estimate of the fixed effects and the analysis of variance (ANOVA) estimates of variance components are proved to be uniformly minimum variance unbiased estimates simultaneously; (ii) the exact confidence intervals of the fixed effects and uniformly optimal unbiased tests on variance components are given; (iii) the exact probability expression of ANOVA estimates of variance components taking negative value is obtained.
文摘Various random models with balanced data that are relevant for analyzing practical test data are described, along with several hypothesis testing and interval estimation problems concerning variance components. In this paper, we mainly consider these problems in general random effect model with balanced data. Exact tests and confidence intervals for a single variance component corresponding to random effect are developed by using generalized p-values and generalized confidence intervals. The resulting procedures are easy to compute and are applicable to small samples. Exact tests and confidence intervals are also established for comparing the random-effects variance components and the sum of random-effects variance components in two independent general random effect models with balanced data. Furthermore, we investigate the statistical properties of the resulting tests. Finally, some simulation results on the type Ⅰ error probability and power of the proposed test are reported. The simulation results indicate that exact test is extremely satisfactory for controlling type Ⅰ error probability.
基金supported by the National Natural Science Foundation of China(32070557 and 31871242)the Fundamental Research Funds for the Central Universities(2662020ZKPY017)+1 种基金the Huazhong Agricultural University Scientific&Technological Self-Innovation Foundation(2014RC020)the State Key Laboratory of Cotton Biology Open Fund(CB2021B01).
文摘Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are imperfect.To address these issues,the mixed model proposed here first estimates the genotypic effects for AA,Aa,and aa,and the genotypic polygenic background replaces additive and dominance polygenic backgrounds.Then,the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model.This strategy was further expanded to cover QTN-by-environment interactions(QEIs)and QTN-by-QTN interactions(QQIs)using the same mixed-model framework.Thus,a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model(mrMLM)method to establish a new methodological framework,3VmrMLM,that detects all types of loci and estimates their effects.In Monte Carlo studies,3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects,with high powers and accuracies and a low false positive rate.In re-analyses of 10 traits in 1439 rice hybrids,detection of 269 known genes,45 known gene-by-environment interactions,and 20 known gene-by-gene interactions strongly validated 3VmrMLM.Further analyses of known genes showed more small(67.49%),minor-allele-frequency(35.52%),and pleiotropic(30.54%)genes,with higher repeatability across datasets(54.36%)and more dominance loci.In addition,a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs,and variable selection under a polygenic background was proposed for QQI detection.This study provides a new approach for revealing the genetic architecture of quantitative traits.
