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Various statistical techniques exist in statistics for the analysis and interpretation of the data. The awareness of assumptions and circumstances governing each statistical method is crucial for selecting the best method for data analysis. The purpose of the study, the type of data, and the measurements (paired or unpaired) are the three aspects to decide the most appropriate statistical method. Statistical procedures that compare means are known as parametric methods, and those statistical techniques that reach median, mean ranks, or proportions are known as nonparametric techniques. The current review aims to help the reader understand the assumptions underlying both parametric and non-parametric statistical approaches and to choose the best ones for the analysis and interpretation of biomedical data.

Recent advancements in deep learning have enabled 3D human body reconstruction from a monocular image, which has broad applications in multiple domains. In this paper, we propose SHARP (SHape Aware Reconstruction of People in loose clothing), a novel end-to-end trainable network that accurately recovers the 3D geometry and appearance of humans in loose clothing from a monocular image. SHARP uses a sparse and efficient fusion strategy to combine parametric body prior with a non-parametric 2D representation of clothed humans. The parametric body prior enforces geometrical consistency on the body shape and pose, while the non-parametric representation models loose clothing and handles self-occlusions as well. We also leverage the sparseness of the non-parametric representation for faster training of our network while using losses on 2D maps. Another key contribution is 3DHumans, our new life-like dataset of 3D human body scans with rich geometrical and textural details. We evaluate SHARP on 3DHumans and other publicly available datasets, and show superior qualitative and quantitative performance than existing state-of-the-art methods.

We study the model Y = X + ε. We assume that we have at our disposal independent identically distributed observations Y1...,Yn and ε-1..,e-M. The (Xj)1≤j≤n are independent identically distributed with density fε, independent of the (εj)1≤j≤n,independent identically distributed with density fε. The aim of the paper is to estimate without knowing fε. We first define an estimator, for which we provide bounds for the integrated L² -risk. We consider ordinary smooth and supersmooth noise ε with regard to ordinary smooth and supersmooth densities fε. Then we present an adaptive estimator of the density of fε. This estimator is obtained by penalization of a projection contrast and yields to model selection. Lastly, we present simulation experiments to illustrate the good performances of our estimator and study from the empirical point of view the importance of theoretical constraints.

Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non-parametric Bayesian set-up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on real and simulated data. These new methods, which involve simulating on a manifold, can be applied widely, including providing Bayesian analysis of quasi-likelihoods, linear and non-linear regression, missing data and hierarchical models.

Questions: Many methods have been developed to estimate species richness but few are useful for estimating regional richness. We compared the performance of commonly used non-parametric and area-based estimators with a particular focus on testing a newly developed but little tested maximum entropy method (MaxEnt). Location: Tropical forest of Jianfengling Reserve, Hainan Island, China. Methods: We extrapolated species richness on 12 estimators up to a larger regional scale — the reserve (472 km 2 ) — where 164 25 m × 25 m quadrats were distributed on a grid of 160 km 2 within the tropical forest. We also analysed the effects of base (or 'anchor') scale A 0 on the species richness estimated (S est ) with MaxEnt. Results: Six non-parametric methods underestimated the species richness, while six area-based methods overestimated the species richness. The accuracy of the MaxEnt estimate (S est ) was improved with the increase of base scale A 0 . Conclusions: Our findings suggest non-parametric methods should not be used to estimate richness across heterogeneous landscapes but can be used in well-defined sampling areas. Jack2 is the best of the six non-parametric methods, while the logistic model and the MaxEnt method seem to be the best of the six area-based methods. Improvements to the MaxEnt method are possible but that will require reformulation of the method by considering species—abundance distributions other than log-series and more general spatial allocation rules.

The paper develops non-parametric methods based on contact intervals for the analysis of infectious disease data. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. The hazard function of the contact interval distribution equals the hazard of infectious contact from i to j, so it provides a summary of the evolution of infectiousness over time. When who infects whom is observed, the Nelson—Aalen estimator produces an unbiased estimate of the cumulative hazard function of the contact interval distribution. When who infects whom is not observed, we use an expectation—maximization algorithm to average the Nelson—Aalen estimates from all possible combinations of who infected whom consistent with the observed data. This converges to a non-parametric maximum likelihood estimate of the cumulative hazard function that we call the marginal Nelson—Aalen estimate. We study the behaviour of these methods in simulations and use them to analyse household surveillance data from the 2009 influenza A(H1N1) pandemic.

This article extends the Chavas-Cox approach to non-parametric analysis by incorporating undesirable outputs to provide a more complete representation of the production technology. Inner and outer non-parametric technology bounds are constructed. The methods are illustrated with application to time series data for the Canadian pulp and paper industry. Conventional measures that ignore changes in pollutant outputs underestimate true productivity growth. Further, there is a large gap between estimates generated with reference to inner and outer bounds to the technology, suggesting that researchers need to be aware of the limitations of results derived from analyses relying only on DEA methods.

The rise in healthcare expenditures has raised doubts about the sustainability of health systems and instigated a discussion on their design. Policy making in this field requires a proper understanding of how healthcare expenditures evolve throughout an individual's lifetime, and of how they vary between individuals. Given the lack of data on healthcare expenditures during an individual's lifetime, we developed a new nearest neighbour resampling approach to construct realistic individual life cycles of healthcare expenditures based on cross-sectional data from the Netherlands. This approach provides insight into lifetime healthcare expenditures. Our main finding is that the inequality in lifetime healthcare expenditures is much smaller than the inequality as derived from cross-sectional healthcare expenditures.

Motivated by the recent acquired immune deficiency syndrome clinical trial study A5175, we propose a semiparametric framework to describe time-to-event data, where only the dependence of the mean and variance of the time on the covariates are specified through a restricted moment model. We use a second-order semiparametric efficient score combined with a non-parametric imputation device for estimation. Compared with an imputed weighted least squares method, the approach proposed improves the efficiency of the parameter estimation whenever the third moment of the error distribution is non-zero. We compare the method with a parametric survival regression method in the A5175 study data analysis. In the data analysis, the method proposed shows a better fit to the data with smaller mean-squared residuals. In summary, this work provides a semiparametric framework in modelling and estimation of survival data. The framework has wide applications in data analysis.

Background Asthma is one of the leading chronic illnesses among children in the United States. Asthma prevalence is higher among African Americans (11.2%) compared to European Americans (7.7%). Bronchodilator medications are part of the first-line therapy, and the rescue medication, for acute asthma symptoms. Bronchodilator drug response (BDR) varies substantially among different racial/ethnic groups. Asthma prevalence in African Americans is only 3.5% higher than that of European Americans, however, asthma mortality among African Americans is four times that of European Americans; variation in BDR may play an important role in explaining this health disparity. To improve our understanding of disparate health outcomes in complex phenotypes such as BDR, it is important to consider interactions between environmental and biological variables. Results We evaluated the impact of pairwise and three-variable interactions between environmental, social, and biological variables on BDR in 233 African American youth with asthma using Visualization of Statistical Epistasis Networks (ViSEN). ViSEN is a non-parametric entropy-based approach able to quantify interaction effects using an information-theory metric known as Information Gain (IG). We performed analyses in the full dataset and in sex-stratified subsets. Our analyses identified several interaction models significantly, and suggestively, associated with BDR. The strongest interaction significantly associated with BDR was a pairwise interaction between pre-natal smoke exposure and socioeconomic status (full dataset IG: 2.78%, p  = 0.001; female IG: 7.27%, p  = 0.004)). Sex-stratified analyses yielded divergent results for females and males, indicating the presence of sex-specific effects. Conclusions Our study identified novel interaction effects significantly, and suggestively, associated with BDR in African American children with asthma. Notably, we found that all of the interactions identified by ViSEN were “pure” interaction effects, in that they were not the result of strong main effects on BDR, highlighting the complexity of the network of biological and environmental factors impacting this phenotype. Several associations uncovered by ViSEN would not have been detected using regression-based methods, thus emphasizing the importance of employing statistical methods optimized to detect both additive and non-additive interaction effects when studying complex phenotypes such as BDR. The information gained in this study increases our understanding and appreciation of the complex nature of the interactions between environmental and health-related factors that influence BDR and will be invaluable to biomedical researchers designing future studies.