Using Social Network Information for Survey Estimation

Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests

Social Networks have experienced increased popularity and rapid growth in recent years. Recommendation is significant for users due to the extremely large amount of information in Social Networks. Most existing recommender systems rely on collaborative filtering techniques which focus on recommending the most relevant items to users based on past rating information of users or items. In Social Networks, the cold-start and data sparsity problems are very serious because new users and items are growing rapidly. Taking the Event Recommendation problem in Event-Based Social Networks as a scenario, many events are newly created and have few feedbacks. Existed collaborative filtering based methods will fail for Social Recommendation due to these problems. Therefore, a more sophisticated recommendation mechanism that can efficiently combine various contextual information to further improve recommendation quality is desired. In this paper, we propose a Context Aware Matrix Factorization model called CAMF which models implicit feedbacks and various contextual information simultaneously for Social Recommendation. Specifically, CAMF is a unified model that combines the Matrix Factorization model which models implicit feedbacks with the Linear Contextual Features model which models explicit contextual features. Extensive experiments on a large real-world dataset demonstrate that the CAMF model significantly outperforms state-of-the-art methods by 12.7% in terms of accuracy for the Event Recommendation problem.

Soybean [Glycine max (L.) Merril] is one of the main crops produced worldwide, and on‐farm yields have increased considerably in the last decades in Brazil. We evaluated the genetic gain for agronomic, phenological, and end‐use quality traits in 29 cultivars in the South Region, and in 38 cultivars in the Midwest Region in Brazil, released from 1966 to 2011. Field trials were conducted in Macroregions 1, 2, and 4, in 2016–2017, 2017–2018, and 2018–2019 crop seasons. The best linear unbiased predictors (BLUP) of the cultivars were obtained for each trait using a linear model. The BLUPs were regressed with the year of release using linear and quadratic regression models. The rates of genetic gain for seed yield ranged from 11.98 to 15.31 kg ha–1 yr–1 (0.33 to 0.42% yr–1) in the South Region, and from 13.58 to 21.84 kg ha–1 yr–1 (0.47 to 0.77% yr–1) in the Midwest Region. New cultivars presented taller plants and more seed oil content, oil and protein yield, and lower seed weight, days to flowering, days to maturity, and seed protein content than old cultivars in the South Region, although with differences between the Macroregions. In the Midwest Region, new cultivars showed higher seed oil content, oil and protein yield, and lower bottom pod height and seed protein content than old cultivars. Our results showed that breeding programs have been efficient to improve soybean yield and other traits across the years, without yield plateaus in sight.

We collected 3180 records of oleic acid (C18:1) and monounsaturated fatty acid (MUFA) measured using gas chromatography (GC) and 6960 records of C18:1 and MUFA measured using near-infrared spectroscopy (NIRS) in intermuscular fat samples of Japanese Black cattle. We compared genomic prediction performance for four linear models (genomic best linear unbiased prediction [GBLUP], kinship-adjusted multiple loci [KAML], BayesC, and BayesLASSO) and five machine learning models (Gaussian kernel [GK], deep kernel [DK], random forest [RF], extreme gradient boost [XGB], and convolutional neural network [CNN]). For GC-based C18:1 and MUFA, KAML showed the highest accuracies, followed by BayesC, XGB, DK, GK, and BayesLASSO, with more than 6% gain of accuracy by KAML over GBLUP. Meanwhile, DK had the highest prediction accuracy for NIRS-based C18:1 and MUFA, but the difference in accuracies between DK and KAML was slight. For all traits, accuracies of RF and CNN were lower than those of GBLUP. The KAML extends GBLUP methods, of which marker effects are weighted, and involves only additive genetic effects; whereas machine learning methods capture non-additive genetic effects. Thus, KAML is the most suitable method for breeding of fatty acid composition in Japanese Black cattle.

The best linear unbiased estimator (BLUE) and the best linear unbiased predictor (BLUP) are used to estimate, respectively, the parameter vectors of fixed and random effects in linear mixed models. But, when multicollinearity problem is arised, alternative estimators and predictors to BLUE and BLUP are preferred because of bad variance property of BLUE. Commonly used prediction approaches are the ridge and Liu prediction under multicollinearityin linear mixed models and in this article, we suggest a new prediction approachto combat multicollinearity problem by expanding the Kibria–Lukman (KL) prediction approach in linear regression models to linear mixed models. We do comparisons between the KL estimator/predictor and several other estimators/predictors, namely BLUE/BLUP, ridge and Liu estimators/predictors by using the matrix mean square error criterion. We give the selection of the ridge biasing parameter. Lastly, to demonstrate the performance of our new suggested prediction approach, we make greenhouse gases data analysis and a Monte-Carlo simulation study.

Supervised learning, e.g., classification, plays an important role in processing and organizing microblogging data. In microblogging, it is easy to mass vast quantities of unlabeled data, but would be costly to obtain labels, which are essential for supervised learning algorithms. In order to reduce the labeling cost, active learning is an effective way to select representative and informative instances to query for labels for improving the learned model. Different from traditional data in which the instances are assumed to be independent and identically distributed (i.i.d.), instances in microblogging are networked with each other. This presents both opportunities and challenges for applying active learning to microblogging data. Inspired by social correlation theories, we investigate whether social relations can help perform effective active learning on networked data. In this paper, we propose a novel Active learning framework for the classification of Networked Texts in microblogging (ActNeT). In particular, we study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from microblogging for labeling by taking advantage of social network structure. Experimental results on Twitter datasets show the benefit of incorporating network information in active learning and that the proposed framework outperforms existing state-of-the-art methods.

A small area is an area with small sample size to estimate parameters in survey sampling. The direct estimation will produce inaccurate estimation since the sample size is not enough to produce estimation with acceptable precision. Small Area Estimation (SAE) is a solution to obtain more precise estimation in a small area. A well-known method in SAE is an empirical best linear unbiased prediction (EBLUP). EBLUP is the estimator of small area means. It will provide an accurate estimation under normality assumptions but it can be sensitive when the data are contaminated by outliers. In this article, we discussed a resistant method in SAE, i.e. robust empirical best linear unbiased prediction (REBLUP). We apply REBLUP from unit-level models to the data obtained from the National Socio-economic Survey (SUSENAS). The means of per capita expenditures are calculated for all small areas. We compare the estimates of per capita expenditure in the small area using direct estimation, EBLUP and REBLUP methods using data that contain outliers. The result shows that REBLUP estimation has produced more accurate estimates when compared to the other methods.

Exponomial forecasts of nonstationary time series

Parameter estimation performance for sigmoid emax models in exposure-response Relationship

The contribution of context information: A case study of object recognition in an intelligent car

Assume that a pair of general Linear Random-effects Models (LRMs) are given with a correlated covariance matrix for their error terms. This paper presents an algebraic approach to the statistical analysis and inference of the two correlated LRMs using some state-of-the-art formulas in linear algebra and matrix theory. It is shown first that the best linear unbiased predictors (BLUPs) of all unknown parameters under LRMs can be determined by certain linear matrix equations, and thus the BLUPs under the two LRMs can be obtained in exact algebraic expressions. We also discuss algebraical and statistical properties of the BLUPs, as well as some additive decompositions of the BLUPs. In particular, we present necessary and sufficient conditions for the separated and simultaneous BLUPs to be equivalent. The whole work provides direct access to a very simple algebraic treatment of predictors/estimators under two LRMs with correlated covariance matrices.

Correlation among the observations in high-dimensional regression modeling can be a major source of confounding. We present a new open-source package, plmmr, to implement penalized linear mixed models in R. This R package estimates correlation among observations in high-dimensional data and uses those estimates to improve prediction with the best linear unbiased predictor. The package uses memory mapping so that genome-scale data can be analyzed on ordinary machines even if the size of data exceeds random-access memory. We present here the methods, workflow, and file-backing approach upon which plmmr is built, and we demonstrate its computational capabilities with two examples from real genome-wide association studies data.

Inference regarding the inclusion or exclusion of random effects in linear mixed models is challenging because the variance components are located on the boundary of their parameter space under the usual null hypothesis. As a result, the asymptotic null distribution of the Wald, score, and likelihood ratio tests will not have the typical χ(2) distribution. Although it has been proved that the correct asymptotic distribution is a mixture of χ(2) distributions, the appropriate mixture distribution is rather cumbersome and nonintuitive when the null and alternative hypotheses differ by more than one random effect. As alternatives, we present two permutation tests, one that is based on the best linear unbiased predictors and one that is based on the restricted likelihood ratio test statistic. Both methods involve weighted residuals, with the weights determined by the among- and within-subject variance components. The null permutation distributions of our statistics are computed by permuting the residuals both within and among subjects and are valid both asymptotically and in small samples. We examine the size and power of our tests via simulation under a variety of settings and apply our test to a published data set of chronic myelogenous leukemia patients.

India being a vegetarian country, milk is the major source of dietary bio-energy, but majority of animals are routinely being evaluated on the basis of their milk producing ability. The present study was aimed to come up with a sire evaluation methodology based on first lactation Fat Energy Corrected Milk Yield (FBE) in order to obtain an accurate and unbiased estimate of breeding value of Mehsana buffalo bulls and ranking them on the basis of their daughter's performance for future herd improvement. Data for the present study included 7825 she buffaloes in their first lactations, extended over a period of 25 years (1989 to 2013), from field progeny testing programme of Dudhsagar Research and Development Association (DURDA), Dudhsagar Dairy, Mehsana, Gujarat. The data were classified into different subclasses based on period, season, cluster and age at first calving group. The average breeding values of Mehsana buffalo bulls evaluated for FBE by least squares method (LSM), best linear unbiased prediction sire model (BLUP-SM) and best linear unbiased prediction animal model (BLUP-AM) methods were 1215.89, 1185.7 and 1185.7 kcal, respectively. BLUP-AM method had lowest error variance as compared to LSM and BLUP-SM methods of sire evaluation. This indicated that BLUP-AM was most efficient method.

Standardized Feeding Approach Mitigates Weight Loss in Infants with Congenital Heart Disease