Tillé, Yves

2019-6-12, Tillé, Yves, Vallée, Audrey-Anne

2018-11-27, Tillé, Yves

2018-10-26, Tillé, Yves

2018-9-20, Vallée, Audrey-Anne, Tillé, Yves

Swiss cheese nonresponse or non-monotone nonresponse occurs when all the variables of a survey can contain missing values without a particular pattern. Imputation of missing values allows to reduce the bias and the variability due to nonresponse. It is difficult to preserve the distributions and the relations between the variables when imputing in the swiss cheese nonresponse case. In this presentation, balanced K-nearest neighbor imputation Hasler and Tillé (2016) is extended to treat swiss cheese nonresponse. It is a donor imputation method that is random and constructed to meet some requirements. First, a nonrespondent can be imputed by donors which are close to him. The distances are calculated with the observed values. Next, all the missing values of a nonrespondent are imputed by the same donor. Last, the donors are chosen so that if the observed values of the nonrespondents were imputed, the estimated totals would be the same as the one calculated with the observed values only. To meet all the requirements, a matrix of imputation probabilities is constructed with calibration techniques. The donors are selected with these imputation probabilities and balanced sampling methods. The advantages and the properties of the method are investigated in a simulation study.

2019, Ecker, Klaus, Meier, Eliane, Lanz, Adrian, Tillé, Yves

We describe a complex probability sampling design for a long-term monitoring program of agricultural species and habitats in Switzerland. The program aims to monitor farmland biodiversity in predefined regions and to assess the effectiveness of funded management in promoting it. Such monitoring requires the costly collection of {\it in situ} information on species, habitat types and structures at the plot and landscape level. Sample efficiency is challenging since the majority of habitats and species is typically rare, spatially structured and previously unknown in the sampling frame. Efficient sampling aims to minimize the collection of redundant information from the big regions and the dominant habitat types. The sample should be spatially spread and balanced across environmental gradients. Decisions should be made to allocate the sampling effort within and across sample sites. Finally, the survey organization has to be simple to implement in the field. In Switzerland zoological data are already collected on a regular grid of 1 km$^2$. We propose an additional three-stage sampling scheme for the static survey of habitats and plant species on the total agrarian landscape. An extra sample scheme is defined to monitor areas with funded biodiversity management. Both sampling designs use modern sampling techniques, such as unequal probability sampling, balancing, spatial spreading and self-weighting to ensure sample efficiency at all sampling stages. The efficiency of balancing, spreading and sample size allocation is demonstrated in simulation studies. A power analysis suggests that changes of $5-10\%$ can be statistically detected for a majority of the target habitats.

2018-11-27, Tillé, Yves

2018-10-11, Tillé, Yves

2018-12-6, Tillé, Yves

2018-11-27, Tillé, Yves

2018-10-4, Tillé, Yves