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Misclassifications in binary responses have long been a common problem in medical and health surveys. One way to handle misclassifications in clustered or longitudinal data is to incorporate the misclassification model through the generalized estimating equation (GEE) approach. However, existing methods are developed under a non-survey setting and cannot be used directly for complex survey data. We propose a pseudo-GEE method for the analysis of binary survey responses with misclassifications. We focus on cluster sampling and develop analysis strategies for analyzing binary survey responses with different forms of additional information for the misclassification process. The proposed methodology has several attractive features, including simultaneous inferences for both the response model and the association parameters. Finite sample performance of the proposed estimators is evaluated through simulation studies and an application using a real dataset from the Canadian Longitudinal Study on Aging. Les mauvaises classifications pour une variable réponse binaire donnée constituent un problème commun dans les enquêtes médicales. En présence de données longitudinales ou en grappe, une façon de traiter cette problématique consiste à incorporer un modèle de mauvaise classification à une approche par équations d’estimation généralisées (EEG). Les méthodes existantes n’ont toutefois pas été conçues pour des données d’enquêtes et ne peuvent donc pas être utilisées directement pour de telles données. Les auteurs proposent une méthode pseudo-EEG pour l’analyse de réponses binaires dans les enquêtes comportant de la mauvaise classification. Ils se concentrent sur l’échantillonnage par grappe et développent des stratégies pour analyser les réponses binaires en exploitant différentes formes d’information additionnelle à propos du processus de mauvaise classification. La méthodologie proposée comporte de nombreuses caractéristiques attrayantes, notamment la capacité d’inférer simultanément le modèle de réponse et les paramètres d’association. Les auteurs évaluent les performances de leur approche sur des échantillons finis par des études de simulation et une application à des données réelles de l’Étude longitudinale canadienne sur le vieillissement (ÉLCV).

1. The impact of hen harriers Circus cyaneus on red grouse Lagopus lagopus scoticus populations has received much attention. However, little has been done to model the population dynamics of the hen harrier alone. Such a model is needed to help inform the differing aims of conserving harriers and managing grouse moors, which serves as a reflection of human-wildlife conflicts around the globe. 2. On Langholm estate in Scotland, intensive studies have resulted in harrier numbers being known without error. We fit a Bayesian population model to these data, using a super-population model to permit inference in the presence of demographic and environmental stochasticity and in the absence of observation error. 3. Hen harriers have a straightforward life history. After fledging, juveniles show little natal site fidelity, often dispersing long distances into breeding areas rich in their preferred prey, the field vole Microtus agrestis and meadow pipit Anthus pratensis. Therefore, any increase in a local population is largely because of recruitment into the area as opposed to fledging success. Once birds have settled in an area, harriers are generally site faithful, with year-to-year survival depending, in part, on the density of meadow pipits. 4. Our model suggests that temporal patterns in harrier numbers on managed grouse moors, in the absence of illegal persecution, are influenced by vole numbers, whereas meadow pipit density appears to have a limited effect. 5. Synthesis and applications. Our modelling approach is a useful way to infer population processes, and the effects of the environment on these processes, for populations censused without error. When used to predict future harrier numbers under alternate management scenarios, our model indicates that harrier numbers on Langholm estate, Scotland, could be reduced without any direct human intervention if the estate can be managed in a way that reduces vole populations. In contrast, there appears little to gain from managing meadow pipit densities. If these conclusions apply to other harrier populations, then management to reduce vole numbers, while maintaining grouse densities, may help alleviate the conflict between conservationists and managers of grouse moors.

Multi-level models provide a convenient framework for analyzing data from survey samples with hierarchical structures. Inferential procedures that take account of survey design features are well established for single-level (or marginal) models. However, methods that are valid for general multi-level models are somewhat limited. This paper presents a unified method for two-level models, based on a weighted composite likelihood approach, that takes account of design features and provides valid inferences even for small sample sizes within level 2 units. The proposed method has broad applicability and is straightforward to implement. Empirical studies have demonstrated that the method performs well in estimating the model parameters. Moreover, this research has an important implication: it provides a particular scenario to showcase the unique merit of the composite likelihood method where the likelihood method would not work.

This paper develops statistical inference for population mean and total using stratified judgment post-stratified (SJPS) samples. The SJPS design selects a judgment post-stratified sample from each stratum. Hence, in addition to stratum structure, it induces additional ranking structure within stratum samples. SJPS is constructed from a finite population using either a with or without replacement sampling design. Inference is constructed under both randomization theory and a super population model. In both approaches, the paper shows that the estimators of population mean and total are unbiased. The paper also constructs unbiased estimators for the variance (mean square prediction error) of the sample mean (predictor of population mean), and develops confidence and prediction intervals for the population mean. The empirical evidence shows that the proposed estimators perform better than their competitors in the literature.

Consider a finite population u, which can be viewed as a realization of a super-population model. A simple ratio model (linear regression, without intercept) with heteroscedastic errors is supposed to have generated u. A random sample is drawn without replacement from u. In this set-up a two-stage wild bootstrap resampling scheme as well as several other useful forms of bootstrapping in finite populations will be considered. Some asymptotic results for various bootstrap approximations for normalized and Studentized versions of the well-known ratio and regression estimator are given. Bootstrap based confidence intervals for the population total and for the regression parameter of the underlying ratio model are also discussed.

In the present investigation, we have made first shot to propose an alternative estimator of population total, in the presence of random non-response, for multi-characteristics by using probability proportional to size and without replacement (PPSWOR) sampling when the selection probabilities are poorly correlated with the study variables. The mean square error (MSE) expressions are derived for the proposed estimator. The behavior of the proposed estimator has been examined under super population model. An empirical study has also been carried out to look into the performance of the proposed estimator. The proposed estimator has been applied to real data set.

The rationale for the use of sample survey weights in a least squares regression analysis is examined with respect to four increasingly general specifications of the population regression model. The appropriateness of the weighted regression estimate depends on which model is chosen. A proposal is made to use the difference between the weighted and unweighted estimates as an aid in choosing the appropriate model and hence the appropriate estimator. When applied to an analysis of the familial and environmental determinants of the educational level attained by a sample of young adults, the methods lead to a revision of the initial additive model in which interaction terms between county unemployment and race, as well as between sex and mother's education, are included.

The problem of optimum allocation to strata has been earlier examined in the light of a priori distributions. In this context, under the criterion of minimum expected variance, the sampling strategy consisting of an unstratified π PS sampling scheme together with the Horvitz-Thompson (HT) estimator was shown to be inferior to the strategy consisting of a stratified π PS sampling scheme with the corresponding HT estimator with this optimum allocation. In this paper, when stratification is based on the auxiliary information, we study whether a stratified π PS sampling strategy with various non-optimal allocations is likely to be worth while and whether it should be attempted at all. For populations commonly met in practice, we derive sufficient conditions for unstratified π PS sampling to be preferable to non-optimal stratified π PS sampling. An illustrative example is provided towards the end of the paper.

Data from ‘citizen science’ surveys are increasingly valuable in identifying declines in widespread species, but require special attention in the case of invertebrates, with considerable variation in number, seasonal flight patterns and, potentially, voltinism. There is a need for reliable and more informative methods of inference in such cases. We focus on data consisting of sample counts of individuals that are not uniquely identifiable, collected at one or more sites. Arrival or emergence and departure or death of individuals take place during the study. We introduce a new modelling approach, which borrows ideas from the ‘stopover’ capture–recapture literature, that permits the estimation of parameters of interest, such as mean arrival times and relative abundance, or in some cases, absolute abundance, and the comparison of these between sites. The model is evaluated using an extensive simulation study which demonstrates that the estimates for the parameters of interest obtained by the model are reliable, even when the data sets are sparse, as is often the case in reality. When applied to data for the common blue butterfly Polyommatus icarus at a large number of sites, the results suggest that mean emergence times, as well as the relative sizes of the broods, are linked to site northing, and confirm field experience that the species is bivoltine in the south of the UK but practically univoltine in the north. Synthesis and applications. Our proposed ‘stopover’ model is parameterized with biologically informative constituents: times of emergence, survival rate and relative brood sizes. Estimates of absolute or relative abundance that can be obtained alongside these underlying variables are robust to the presence of missing observations and can be compared in a statistically rigorous framework. These estimates are direct indices of abundance, rather than ‘sightings’, implicitly adjusted for the possible presence of repeat sightings during a season. At the same time, they provide indices of change in demographic and phenological parameters that may be of use in identifying the factors underlying population change. The model is widely applicable and this will increase the utility of already valuable and influential long‐standing surveys in monitoring the effects of environmental change on phenology or abundance.

Animal movement patterns, whether related to dispersal, migration, or ranging behaviors, vary in time. Individual movements reflect the outcomes of interactions between an individual's condition and a multitude of underlying ecological processes. Theory predicts that when competition for breeding territories is high, individuals should arrive at breeding sites earlier than what would otherwise be optimal for breeding in the absence of competition. This is because priority at a site can confer significant competitive advantages leading to better breeding outcomes. Empirical data from long-distance migrants support this theory. However, it has not been tested within the context of fine-scale movements in nonmigratory populations. We assessed the effect of arrival time at a breeding site on reproductive outcomes in an intensively monitored resident population of Great Tits ( Parus major ). The population was monitored passively, via passive integrated transponder (PIT) tag loggers, and actively, via catching, during breeding and nonbreeding seasons. We developed new capture-recapture-resight models that use both data types to model breeding outcome conditional on the unknown individual arrival times. In accordance with theory, individuals arrived at the woods synchronously in waves that were large at the beginning of the nonbreeding season and small toward the end, with very few arrivals in the intervening period. There was a strong effect of arrival time on the probability of breeding; the earlier an individual arrived, the more likely it was to successfully establish a nest that reached the incubation period. However, once nests were established, they had equal probabilities of failing early, regardless of arrival time. Finally, there was moderate evidence of a negative effect of arrival time on the probability of successfully fledging nestlings. These empirical findings are consistent with theoretical models that suggest an important role for competition in shaping fine-scale seasonal movements. Our capture-recapture-resight models are extensible and suitable for a variety of applications, particularly when the goal is to estimate the effects of unobservable arrival times on subsequent ecological outcomes.