On the Uniformly Most Powerful Invariant Test for the Shoulder Condition in Line Transect Sampling

In wildlife population studies one of the main goals is estimating the population abundance. Line transect sampling is a well established methodology for this purpose. The usual approach for estimating the density or the size of the population of interest is to assume a particular model for the detection function (the conditional probability of detecting an animal given that it is at a given distance from the observer). Two common models for this function are the half-normal model and the negative exponential model. The estimates are extremely sensitive to the shape of the detection function, particularly to the so-called shoulder condition, which ensures that an animal is almost certain to be detected if it is at a small distance from the observer. The half-normal model satisfies this condition whereas the negative exponential does not. Therefore, testing whether such a hypothesis is consistent with the data is a primary concern in every study aiming at estimating animal abundance. In this paper we propose a test for this purpose. This is the uniformly most powerful test in the class of the scale invariant tests. The asymptotic distribution of the test statistic is worked out by utilising both the half-normal and negative exponential model while the critical values and the power are tabulated via Monte Carlo simulations for small samples. .Line Transect Sampling, Shoulder Condition, Uniformly Most Powerful Invariant Test, Asymptotic Critical Values, Monte Carlo Critical Values

The Uniformly Most Powerful Invariant Test for the Shoulder Condition in Point Transect Sampling

Estimating population abundance is of primary interest in wildlife population studies. Point transect sampling is a well established methodology for this purpose. The usual approach for estimating the density or the size of the population of interest is to assume a particular model for the detection function (the conditional probability of detecting an animal given that it is at a given distance from the observer). The two most popular models for this function are the half-normal model and the negative exponential model. However, it appears that the estimates are extremely sensitive to the shape of the detection function, particularly to the so-called shoulder condition, which ensures that an animal is almost certain to be detected if it is at a small distance from the observer. The half-normal model satisfies this condition whereas the negative exponential does not. Therefore, testing whether such a hypothesis is consistent with the data at hand should be a primary concern in every study concerning the estimation of animal abundance. In this paper we propose a test for this purpose. This is the uniformly most powerful test in the class of the scale invariant tests. The asymptotic distribution of the test statistic is calculated by utilising both the half-normal and negative exponential model while the critical values and the power are tabulated via Monte Carlo simulations for small samples. Finally, the procedure is applied to two datasets of chipping sparrows collected at the Rocky Mountain Bird Observatory, Colorado..Point Transect Sampling, Shoulder Condition, Uniformly Most Powerful Invariant Test, Asymptotic Critical Values, Monte Carlo Critical Values

A Geostatistical Approach to Define Guidelines for Radon Prone Area Identification

Radon is a natural radioactive gas known to be the main contributor to natural background radiation exposure and the major leading cause of lung cancer second to smoking. Indoor radon concentration levels of 200 and 400 Bq/m3 are reference values suggested by the 90/143/Euratom recommendation, above which mitigation measures should be taken in new and old buildings, respectively, to reduce exposure to radon. Despite this international recommendation, Italy still does not have mandatory regulations or guidelines to deal with radon in dwellings. Monitoring surveys have been undertaken in a number of western European countries in order to assess the exposure of people to this radioactive gas and to identify radon prone areas. However, such campaigns provide concentration values in each single dwelling included in the sample, while it is often necessary to provide measures of the pollutant concentration which refer to sub-areas of the region under study. This requires a realignment of the spatial data from the level at which they are collected (points) to the level at which they are necessary (areas). This is known as change of support problem. In this paper, we propose a methodology based on geostatistical simulations in order to solve this problem and to identify radon prone areas which may be suggested for national guidelines.Radon Prone Areas, kriging, geostatistical conditional simulation, change of support problem

Fragilità, credibilità, controfattuale

Riassunto: Nell’ultimo decennio il p-value è stato sottoposto a notevoli critiche soprattutto per l’uso che se ne fa per raggiungere una conclusione dicotomica circa la significatività del risultato sperimentale (significativo o non significativo). Pertanto, da una parte il p-value è stato sostituito con approcci differenti, dall’altra è stato affiancato da alcune procedure diagnostiche, tra le quali figurano la fragilità e la credibilità, che hanno il compito di rafforzare o meno la conclusione. La fragilità e l’indice che la misura presentano aspetti di debolezza metodologica. D’altro canto, l’indice di credibilità sembra idoneo per dare o meno supporto alla conclusione e per rafforzare o sostituire l’indice di fragilità, dato che misura la credibilità del risultato osservato quantificando l’informazione a priori necessaria per ribaltare il risultato stesso. Il particolare meccanismo delle due procedure, che si fonda su quanto dovrebbe accadere per cambiare la conclusione, suggerisce di inserire le medesime nella prospettiva controfattuale considerandole come nuovi strumenti per la sua misura quantitativa. In questo contributo si presenta questa prospettiva, con particolare riferimento al campo applicativo delle scienze psicologiche.Parole chiave: p-value; Indice di fragilità; Distribuzioni a priori; Indice di credibilità; Prospettiva controfattuale  Fragility, credibility and counterfactualityAbstract: In the last decade, scientific reliance on p-values, especially when used to determine in a dichotomic manner whether a scientific result is significant or not, has been strongly criticized. As a consequence, p-values are sometimes replaced by other statistical tools, or supplemented by complementary procedures such as tests for fragility and credibility, which lend further support or challenge the conclusion. The fragility index presents some methodological weaknesses of its own. The credibility index proposed in the literature seems to provide a particularly useful supplement for p-values as well as for the fragility index, considering that it assesses the reliability of the result obtained by quantifying the a priori information needed to overturn the result. Both procedures rely on what would need to happen in order to modify the conclusion. This suggests that they can be considered as valuable new tools for quantitative measurement within a counterfactual framework. In our contribution we present this perspective, with reference to the psychological sciences.Keywords: p-value; Fragility Index; Priors/Posteriors; Credibility Index; Counterfactual Perspectiv

Nitrates in drinking water: relation with intensive livestock production

The directive 91/676/CEE aims to reduce the nitrogen pressure in Europe from agriculture sources and identifies the livestock population as one of the predominant sources of surplus of nutrients that could be released in water and air. Directive is concerned about the cattle, sheep, pigs and poultry and their territorial loads, but it does not deal with fish farms. On the basis of multivariate statistical analysis, this paper aims to establish what types of farming affect the presence of nitrates in drinking water in the province of Cuneo, Piedmont, Italy. We have used data from official sources on nitrates in drinking water and data Arvet database, concerning the presence of intensive farming in the considered area. We have identified fish farms as a major source of nitrogen released into the environment, while pollution from sheep and poultry has appeared negligible. Thus we would like to emphasize the need to include in the âNitrate Vulnerable Zonesâ areas in which there are intensive farming of fish with open-system type of water use

Comparative efficacy of three Bayesian variable selection methods in the context of weight loss in obese women

The use of high-dimensional data has expanded in many fields, including in clinical research, thus making variable selection methods increasingly important compared to traditional statistical approaches. The work aims to compare the performance of three supervised Bayesian variable selection methods to detect the most important predictors among a high-dimensional set of variables and to provide useful and practical guidelines of their use. We assessed the variable selection ability of: (1) Bayesian Kernel Machine Regression (BKMR), (2) Bayesian Semiparametric Regression (BSR), and (3) Bayesian Least Absolute Shrinkage and Selection Operator (BLASSO) regression on simulated data of different dimensions and under three scenarios with disparate predictor-response relationships and correlations among predictors. This is the first study describing when one model should be preferred over the others and when methods achieve comparable results. BKMR outperformed all other models with small synthetic datasets. BSR was strongly dependent on the choice of its own intrinsic parameter, but its performance was comparable to BKMR with large datasets. BLASSO should be preferred only when it is reasonable to hypothesise the absence of synergies between predictors and the presence of monotonous predictor-outcome relationships. Finally, we applied the models to a real case study and assessed the relationships among anthropometric, biochemical, metabolic, cardiovascular, and inflammatory variables with weight loss in 755 hospitalised obese women from the Follow Up OBese patients at AUXOlogico institute (FUOBAUXO) cohort

Testing agreement among multiple raters

The aim of this paper is to propose a procedure for testing chance agreement among multiple raters which is based on a X2 statistic. The main advantage of using the X2 test statistic is that it has a well-known limit distribution when either the number of subjects or the number of raters is large

The uniformly most powerful invariant test for two models of detection function in point transect sampling

Estimating population abundance is of primary interest in wildlife population studies. Point transect sampling is a well established methodology for this purpose. The usual approach for estimating the density or the size of the population of interest is to assume a particular model for the detection function (the conditional probability of detecting an animal given that it is at a certain distance from the observer). Two popular models for this function are the half-normal model and the negative exponential model. However, it appears that the estimates are extremely sensitive to the shape of the detection function, particularly to the so-called shoulder condition, which ensures that an animal is nearly certain to be detected if it is at a small distance from the observer. The half-normal model satisfies this condition whereas the negative exponential does not. Testing whether such a hypothesis is consistent with the data at hand should be a primary concern. Given that the problem of testing the shoulder condition of a detection function is invariant under the group of scale transformations, in this paper we propose the uniformly most powerful

The uniformly most powerful invariant test for two models of detection function in point transect sampling

Estimating population abundance is of primary interest in wildlife population studies. Point transect sampling is a well established methodology for this purpose. The usual approach for estimating the density or the size of the population of interest is to assume a particular model for the detection function (the conditional probability of detecting an animal given that it is at a certain distance from the observer). Two popular models for this function are the half-normal model and the negative exponential model. However, it appears that the estimates are extremely sensitive to the shape of the detection function, particularly to the so-called shoulder condition, which ensures that an animal is nearly certain to be detected if it is at a small distance from the observer. The half-normal model satisfies this condition whereas the negative exponential does not. Testing whether such a hypothesis is consistent with the data at hand should be a primary concern. Given that the problem of testing the shoulder condition of a detection function is invariant under the group of scale transformations, in this paper we propose the uniformly most powerful