Beyond Panel Unit Root Tests: Using Multiple Testing to Determine the Non Stationarity Properties of Individual Series in a Panel

Most panel unit root tests are designed to test the joint null hypothesis of a unit root for each individual series in a panel. After a rejection, it will often be of interest to identify which series can be deemed to be stationary and which series can be deemed nonstationary. Researchers will sometimes carry out this classification on the basis of n individual (univariate) unit root tests based on some ad hoc significance level. In this paper, we demonstrate how to use the false discovery rate (FDR) in evaluating I(1)=I(0) classifications based on individual unit root tests when the size of the cross section (n) and time series (T) dimensions are large. We report results from a simulation experiment and illustrate the methods on two data sets.

Mise à l’essai et évaluation qualitative d’une intervention inspirée de la pédagogie narrative destinée aux infirmières soignant des personnes âgées en état confusionnel aigu

Les personnes âgées occupent une proportion importante des lits dans les centres hospitaliers de soins de courte durée québécois et leur présence est en augmentation. Parmi ces personnes, plusieurs présentent un état confusionnel aigu (ÉCA), voire un délirium, au cours de leur hospitalisation. Les soins infirmiers qu’elles requièrent sont complexes et les études portant sur la formation continue des infirmières tiennent peu compte de cette réalité. Les approches utilisées dans les études sont surtout centrées sur l’acquisition de connaissances et d’habiletés techniques et négligent les aspects créatifs, relationnels, critiques, réflexifs et éthiques essentiels à une prestation de soins infirmiers de qualité. On y retrouve également peu d’informations sur la conception de l’intervention éducative et sur son évaluation. C’est dans cette perspective que le but de l’étude était de mettre à l’essai et d’évaluer qualitativement le processus et les résultats d’une intervention éducative auprès d’infirmières soignant des personnes âgées hospitalisées en ÉCA. Plus particulièrement, ce sont les conditions facilitant et contraignant l’intervention, les aspects les plus utiles pour la pratique, les différents savoirs exprimés et les résultats de soins perçus par les participantes qui étaient recherchés. C’est en s’inspirant de la pédagogie narrative de Diekelmann (2001) et des savoirs infirmiers de Chinn et Kramer (2008) que l’intervention a été conçue et évaluée. La description d’expériences de soins vécues par les infirmières et la création d’un environnement d’apprentissage favorisant l’interprétation, en groupe, de ces expériences à l’aide d’informations théoriques et empiriques caractérisent la pédagogie narrative à la base de cette intervention. Pour atteindre le but, une étude de cas a été retenue. La stratégie d’échantillonnage par choix raisonné a permis de sélectionner des participantes travaillant sur les trois quarts de travail, ayant différents niveaux de formation et une expérience comme infirmière variant de huit mois à 36 ans, dont l’âge variait de 23 à 64 ans. L’échantillon, composé de 15 infirmières soignant fréquemment des personnes en ÉCA et travaillant sur des unités de soins chirurgicaux cardiologiques et orthopédiques, était réparti dans trois groupes égaux de cinq participantes. L’intervention éducative comprenait quatre journées de formation offertes à intervalle de trois semaines pour une durée totale de 12 semaines. Au cours de chacune de ces journées, les participantes devaient effectuer un travail écrit réflexif concernant une situation de soins vécue avec une personne en ÉCA et, par la suite, partager, interpréter et s'interroger sur ces situations en faisant des liens avec des informations théoriques et empiriques sur l’ÉCA dans le cadre d’un atelier de groupe. Une triangulation de méthodes de collecte de données, incluant des notes de terrain de l’investigatrice, les travaux réflexifs des participantes, des questionnaires complétés par les participantes après chaque journée de formation et une entrevue individuelle avec chaque participante réalisée par une intervieweuse externe à la fin de l’intervention, a permis de décrire la mise à l’essai de l’intervention et d’évaluer qualitativement son processus et ses résultats. Une analyse de contenu des données qualitatives intra et inter participante a été effectuée. La mise à l’essai de l’intervention a mis en évidence l’importance de tenir compte des besoins variés des participantes et d’ajuster l’intervention éducative d’un groupe à l’autre, notamment eu égard aux contenus théoriques et empiriques sur l’ECA. L’évaluation du processus souligne que l’intervention a été facilitée par les attitudes et la diversité des expériences des participantes, ainsi que par l’utilisation de situations de soins réelles permettant d’intégrer la théorie dans la pratique. L’accès à de nouveaux outils d’évaluation des personnes en ÉCA a été perçu particulièrement utile par les participantes. Quant à l’évaluation des résultats, elle a permis de rendre visibles de nombreux savoirs empiriques, éthiques et esthétiques et certains savoirs personnels et émancipatoires exprimés par les participantes. Les participantes ont, entre autres, réalisé des évaluations plus approfondies des personnes en ÉCA, ont réduit ou évité les mesures de contrôle physiques des personnes atteintes et ont impliqué davantage les familles dans les soins. L’évaluation a aussi permis de décrire certains résultats perçus par les participantes sur le bien-être physique et psychologique des personnes soignées et sur les familles. Les personnes en ECA étaient, entre autres, rassurées, plus calmes et soulagées et les familles moins inquiètes et davantage impliquées dans les soins. Les résultats de l’étude mettent en évidence l’applicabilité d’une intervention éducative narrative basée sur un cadre de référence en sciences infirmières et son utilité pour la formation continue dans les milieux de soins. L’étude ouvre la porte à des possibilités de transfert de l’intervention à d’autres populations d’infirmières soignant des clientèles ayant des besoins complexes, notamment en gériatrie, en oncologie ou en soins palliatifs. Des études visant à évaluer l’intervention auprès d’un échantillon plus important et à explorer ses effets sur les personnes soignées et leurs familles sont proposées.

Whither RDS? An investigation of respondent driven sampling as a method of recruiting mainstream marijuana users.

Background: An important challenge in conducting social research of specific relevance to harm reduction programs is locating hidden populations of consumers of substances like cannabis who typically report few adverse or unwanted consequences of their use. Much of the deviant, pathologized perception of drug users is historically derived from, and empirically supported, by a research emphasis on gaining ready access to users in drug treatment or in prison populations with higher incidence of problems of dependence and misuse. Because they are less visible, responsible recreational users of illicit drugs have been more difficult to study. Methods: This article investigates Respondent Driven Sampling (RDS) as a method of recruiting experienced marijuana users representative of users in the general population. Based on sampling conducted in a multi-city study (Halifax, Montreal, Toronto, and Vancouver), and compared to samples gathered using other research methods, we assess the strengths and weaknesses of RDS recruitment as a means of gaining access to illicit substance users who experience few harmful consequences of their use. Demographic characteristics of the sample in Toronto are compared with those of users in a recent household survey and a pilot study of Toronto where the latter utilized nonrandom self-selection of respondents. Results: A modified approach to RDS was necessary to attain the target sample size in all four cities (i.e., 40 'users' from each site). The final sample in Toronto was largely similar, however, to marijuana users in a random household survey that was carried out in the same city. Whereas well-educated, married, whites and females in the survey were all somewhat overrepresented, the two samples, overall, were more alike than different with respect to economic status and employment. Furthermore, comparison with a self-selected sample suggests that (even modified) RDS recruitment is a cost-effective way of gathering respondents who are more representative of users in the general population than nonrandom methods of recruitment ordinarily produce. Conclusions: Research on marijuana use, and other forms of drug use hidden in the general population of adults, is important for informing and extending harm reduction beyond its current emphasis on 'at-risk' populations. Expanding harm reduction in a normalizing context, through innovative research on users often overlooked, further challenges assumptions about reducing harm through prohibition of drug use and urges consideration of alternative policies such as decriminalization and legal regulation.

Study on period search methods of variable stars: Application to ASAS and CRTS databases

Study on variable stars is an important topic of modern astrophysics. After the invention of powerful telescopes and high resolving powered CCD’s, the variable star data is accumulating in the order of peta-bytes. The huge amount of data need lot of automated methods as well as human experts. This thesis is devoted to the data analysis on variable star’s astronomical time series data and hence belong to the inter-disciplinary topic, Astrostatistics. For an observer on earth, stars that have a change in apparent brightness over time are called variable stars. The variation in brightness may be regular (periodic), quasi periodic (semi-periodic) or irregular manner (aperiodic) and are caused by various reasons. In some cases, the variation is due to some internal thermo-nuclear processes, which are generally known as intrinsic vari- ables and in some other cases, it is due to some external processes, like eclipse or rotation, which are known as extrinsic variables. Intrinsic variables can be further grouped into pulsating variables, eruptive variables and flare stars. Extrinsic variables are grouped into eclipsing binary stars and chromospheri- cal stars. Pulsating variables can again classified into Cepheid, RR Lyrae, RV Tauri, Delta Scuti, Mira etc. The eruptive or cataclysmic variables are novae, supernovae, etc., which rarely occurs and are not periodic phenomena. Most of the other variations are periodic in nature. Variable stars can be observed through many ways such as photometry, spectrophotometry and spectroscopy. The sequence of photometric observa- xiv tions on variable stars produces time series data, which contains time, magni- tude and error. The plot between variable star’s apparent magnitude and time are known as light curve. If the time series data is folded on a period, the plot between apparent magnitude and phase is known as phased light curve. The unique shape of phased light curve is a characteristic of each type of variable star. One way to identify the type of variable star and to classify them is by visually looking at the phased light curve by an expert. For last several years, automated algorithms are used to classify a group of variable stars, with the help of computers. Research on variable stars can be divided into different stages like observa- tion, data reduction, data analysis, modeling and classification. The modeling on variable stars helps to determine the short-term and long-term behaviour and to construct theoretical models (for eg:- Wilson-Devinney model for eclips- ing binaries) and to derive stellar properties like mass, radius, luminosity, tem- perature, internal and external structure, chemical composition and evolution. The classification requires the determination of the basic parameters like pe- riod, amplitude and phase and also some other derived parameters. Out of these, period is the most important parameter since the wrong periods can lead to sparse light curves and misleading information. Time series analysis is a method of applying mathematical and statistical tests to data, to quantify the variation, understand the nature of time-varying phenomena, to gain physical understanding of the system and to predict future behavior of the system. Astronomical time series usually suffer from unevenly spaced time instants, varying error conditions and possibility of big gaps. This is due to daily varying daylight and the weather conditions for ground based observations and observations from space may suffer from the impact of cosmic ray particles. Many large scale astronomical surveys such as MACHO, OGLE, EROS, xv ROTSE, PLANET, Hipparcos, MISAO, NSVS, ASAS, Pan-STARRS, Ke- pler,ESA, Gaia, LSST, CRTS provide variable star’s time series data, even though their primary intention is not variable star observation. Center for Astrostatistics, Pennsylvania State University is established to help the astro- nomical community with the aid of statistical tools for harvesting and analysing archival data. Most of these surveys releases the data to the public for further analysis. There exist many period search algorithms through astronomical time se- ries analysis, which can be classified into parametric (assume some underlying distribution for data) and non-parametric (do not assume any statistical model like Gaussian etc.,) methods. Many of the parametric methods are based on variations of discrete Fourier transforms like Generalised Lomb-Scargle peri- odogram (GLSP) by Zechmeister(2009), Significant Spectrum (SigSpec) by Reegen(2007) etc. Non-parametric methods include Phase Dispersion Minimi- sation (PDM) by Stellingwerf(1978) and Cubic spline method by Akerlof(1994) etc. Even though most of the methods can be brought under automation, any of the method stated above could not fully recover the true periods. The wrong detection of period can be due to several reasons such as power leakage to other frequencies which is due to finite total interval, finite sampling interval and finite amount of data. Another problem is aliasing, which is due to the influence of regular sampling. Also spurious periods appear due to long gaps and power flow to harmonic frequencies is an inherent problem of Fourier methods. Hence obtaining the exact period of variable star from it’s time series data is still a difficult problem, in case of huge databases, when subjected to automation. As Matthew Templeton, AAVSO, states “Variable star data analysis is not always straightforward; large-scale, automated analysis design is non-trivial”. Derekas et al. 2007, Deb et.al. 2010 states “The processing of xvi huge amount of data in these databases is quite challenging, even when looking at seemingly small issues such as period determination and classification”. It will be beneficial for the variable star astronomical community, if basic parameters, such as period, amplitude and phase are obtained more accurately, when huge time series databases are subjected to automation. In the present thesis work, the theories of four popular period search methods are studied, the strength and weakness of these methods are evaluated by applying it on two survey databases and finally a modified form of cubic spline method is intro- duced to confirm the exact period of variable star. For the classification of new variable stars discovered and entering them in the “General Catalogue of Vari- able Stars” or other databases like “Variable Star Index“, the characteristics of the variability has to be quantified in term of variable star parameters.

Development of mechanical methods for cell-tray propagation and field transplanting of dwarf napiergrass (Pennisetum purpureum Schumach.)

Since dwarf napiergrass (Pennisetum purpureum Schumach.) must be propagated vegetatively due to lack of viable seeds, root splitting and stem cuttings are generally used to obtain true-to-type plant populations. These ordinary methods are laborious and costly, and are the greatest barriers for expanding the cultivation area of this crop. The objectives of this research were to develop nursery production of dwarf napiergrass in cell trays and to compare the efficiency of mechanical versus manual methods for cell-tray propagation and field transplanting. After defoliation of herbage either by a sickle (manually) or hand-mowing machine, every potential aerial tiller bud was cut to a single one for transplanting into cell trays as stem cuttings and placed in a glasshouse over winter. The following June, nursery plants were trimmed to a 25–cm length and transplanted in an experimental field (sandy soil) with 20,000 plants ha^(−1) either by shovel (manually) or Welsh onion planter. Labour time was recorded for each process. The manual defoliation of plants required 44% more labour time for preparing the stem cuttings (0.73 person-min. stemcutting^(−1)) compared to using hand-mowing machinery (0.51 person-min. stem-cutting^(−1)). In contrast, labour time for transplanting required an extra 0.30 person-min. m^(−2) (14%) using the machinery compared to manual transplanting, possibly due to the limited plot size for machinery operation. The transplanting method had no significant effect on plant establishment or plant growth, except for herbage yield 110 days after planting. Defoliation of herbage by machinery, production using a cell-tray nursery and mechanical transplanting reduced the labour intensity of dwarf napiergrass propagation.

Sampling uncertainties in surface radiation budget calculations in RADAGAST

In the Radiative Atmospheric Divergence Using ARM Mobile Facility GERB and AMMA Stations (RADAGAST) project we calculate the divergence of radiative flux across the atmosphere by comparing fluxes measured at each end of an atmospheric column above Niamey, in the African Sahel region. The combination of broadband flux measurements from geostationary orbit and the deployment for over 12 months of a comprehensive suite of active and passive instrumentation at the surface eliminates a number of sampling issues that could otherwise affect divergence calculations of this sort. However, one sampling issue that challenges the project is the fact that the surface flux data are essentially measurements made at a point, while the top-of-atmosphere values are taken over a solid angle that corresponds to an area at the surface of some 2500 km2. Variability of cloud cover and aerosol loading in the atmosphere mean that the downwelling fluxes, even when averaged over a day, will not be an exact match to the area-averaged value over that larger area, although we might expect that it is an unbiased estimate thereof. The heterogeneity of the surface, for example, fixed variations in albedo, further means that there is a likely systematic difference in the corresponding upwelling fluxes. In this paper we characterize and quantify this spatial sampling problem. We bound the root-mean-square error in the downwelling fluxes by exploiting a second set of surface flux measurements from a site that was run in parallel with the main deployment. The differences in the two sets of fluxes lead us to an upper bound to the sampling uncertainty, and their correlation leads to another which is probably optimistic as it requires certain other conditions to be met. For the upwelling fluxes we use data products from a number of satellite instruments to characterize the relevant heterogeneities and so estimate the systematic effects that arise from the flux measurements having to be taken at a single point. The sampling uncertainties vary with the season, being higher during the monsoon period. We find that the sampling errors for the daily average flux are small for the shortwave irradiance, generally less than 5 W m−2, under relatively clear skies, but these increase to about 10 W m−2 during the monsoon. For the upwelling fluxes, again taking daily averages, systematic errors are of order 10 W m−2 as a result of albedo variability. The uncertainty on the longwave component of the surface radiation budget is smaller than that on the shortwave component, in all conditions, but a bias of 4 W m−2 is calculated to exist in the surface leaving longwave flux.

Relative proportions of polycyclic aromatic hydrocarbons differ between accumulation bioassays and chemical methods to predict bioavailability

Chemical methods to predict the bioavailable fraction of organic contaminants are usually validated in the literature by comparison with established bioassays. A soil spiked with polycyclic aromatic hydrocarbons (PAHs) was aged over six months and subjected to butanol, cyclodextrin and tenax extractions as well as an exhaustive extraction to determine total PAH concentrations at several time points. Earthworm (Eisenia fetida) and rye grass root (Lolium multiflorum) accumulation bioassays were conducted in parallel. Butanol extractions gave the best relationship with earthworm accumulation (r2 ≤ 0.54, p ≤ 0.01); cyclodextrin, butanol and acetone–hexane extractions all gave good predictions of accumulation in rye grass roots (r2 ≤ 0.86, p ≤ 0.01). However, the profile of the PAHs extracted by the different chemical methods was significantly different (p < 0.01) to that accumulated in the organisms. Biota accumulated a higher proportion of the heavier 4-ringed PAHs. It is concluded that bioaccumulation is a complex process that cannot be predicted by measuring the bioavailable fraction alone. The ability of chemical methods to predict PAH accumulation in Eisenia fetida and Lolium multiflorum was hindered by the varied metabolic fate of the different PAHs within the organisms.

Methods and resources for climate impacts research: achieving synergy

The prediction of climate variability and change requires the use of a range of simulation models. Multiple climate model simulations are needed to sample the inherent uncertainties in seasonal to centennial prediction. Because climate models are computationally expensive, there is a tradeoff between complexity, spatial resolution, simulation length, and ensemble size. The methods used to assess climate impacts are examined in the context of this trade-off. An emphasis on complexity allows simulation of coupled mechanisms, such as the carbon cycle and feedbacks between agricultural land management and climate. In addition to improving skill, greater spatial resolution increases relevance to regional planning. Greater ensemble size improves the sampling of probabilities. Research from major international projects is used to show the importance of synergistic research efforts. The primary climate impact examined is crop yield, although many of the issues discussed are relevant to hydrology and health modeling. Methods used to bridge the scale gap between climate and crop models are reviewed. Recent advances include large-area crop modeling, quantification of uncertainty in crop yield, and fully integrated crop–climate modeling. The implications of trends in computer power, including supercomputers, are also discussed.

Measuring root traits in barley (Hordeum vulgare ssp vulgare and ssp spontaneum) seedlings using gel chambers, soil sacs and X-ray microtomography

Root characteristics of seedlings of five different barley genotypes were analysed in 2D using gel chambers, and in 3D using soil sacs that were destructively harvested and pots of soil that were assessed non-invasively using X-ray microtomography. After 5 days, Chime produced the greatest number of root axes (similar to 6) and Mehola significantly less (similar to 4) in all growing methods. Total root length was longest in GSH01915 and shortest in Mehola for all methods, but both total length and average root diameter were significantly larger for plants grown in gel chambers than those grown in soil. The ranking of particular growth traits (root number, root angular spread) of plants grown in gel plates, soil sacs and X-ray pots was similar, but plants grown in the gel chambers had a different order of ranking for root length to the soil-grown plants. Analysis of angles in soil-grown plants showed that Tadmore had the most even spread of individual roots and Chime had a propensity for non-uniform distribution and root clumping. The roots of Mehola were less well spread than the barley cultivars supporting the suggestion that wild and landrace barleys tend to have a narrower angular spread than modern cultivars. The three dimensional analysis of root systems carried out in this study provides insights into the limitations of screening methods for root traits and useful data for modelling root architecture.

The Representative Soil Sampling Scheme of England and Wales: a statistical analysis of topsoil nutrient status and pH between 1971 and 2001

The Representative Soil Sampling Scheme of England and Wales has recorded information on the soil of agricultural land in England and Wales since 1969. It is a valuable source of information about the soil in the context of monitoring for sustainable agricultural development. Changes in soil nutrient status and pH were examined over the period 1971-2001. Several methods of statistical analysis were applied to data from the surveys during this period. The main focus here is on the data for 1971, 1981, 1991 and 2001. The results of examining change over time in general show that levels of potassium in the soil have increased, those of magnesium have remained fairly constant, those of phosphorus have declined and pH has changed little. Future sampling needs have been assessed in the context of monitoring, to determine the mean at a given level of confidence and tolerable error and to detect change in the mean over time at these same levels over periods of 5 and 10 years. The results of a non-hierarchical multivariate classification suggest that England and Wales could be stratified to optimize future sampling and analysis. To monitor soil quality and health more generally than for agriculture, more of the country should be sampled and a wider range of properties recorded.

Comparative effect of root-knot nematode on severity of Verticillium and Fusarium wilt in cotton

The effect of root-knot nematode (RKN) (Meloidogyne incognita) on Verticillium dahliae and Fusarium oxysporum f.sp. vasinfectum in cotton (Gossypium hirsutum) was investigated. Two different inoculation methods were used, one in which inoculum was added to the soil, so that nematode and fungal inoculum were in close proximity; the other, inoculation into the stem, whereby the two inocula were spatially separated. Invasion of the roots by RKN enhanced disease severity, as measured by the height of vascular browning in the stem, following inoculation with either wilt pathogen. The effect of RKN on Fusarium wilt was more pronounced than that on Verticillium wilt. Nematode-enhanced infection by F. oxysporum is a well known effect but there are few reports of enhanced infection by Verticillium due to RKN. Relative resistance of a number of cotton cultivars to both wilt diseases, as measured by height of vascular browning, was similar to the known field performance of the cultivars. The use of vascular browning as an estimate of disease severity was therefore validated.

Variance estimation for systematic sampling from deliberately ordered populations

The systematic sampling (SYS) design (Madow and Madow, 1944) is widely used by statistical offices due to its simplicity and efficiency (e.g., Iachan, 1982). But it suffers from a serious defect, namely, that it is impossible to unbiasedly estimate the sampling variance (Iachan, 1982) and usual variance estimators (Yates and Grundy, 1953) are inadequate and can overestimate the variance significantly (Särndal et al., 1992). We propose a novel variance estimator which is less biased and that can be implemented with any given population order. We will justify this estimator theoretically and with a Monte Carlo simulation study.

The best of both worlds: Producing national statistics using participatory methods

This paper presents our experience with combining statistical principles and participatory methods to generate national statistics. The methodology was developed in Malawi during 1999–2002. We demonstrate that if PRA is combined with statistical principles (including probability-based sampling and standardization), it can produce total population statistics and estimates of the proportion of households with certain characteristics (e.g., poverty). It can also provide quantitative data on complex issues of national importance such as poverty targeting. This approach is distinct from previous PRA-based approaches, which generate numbers at community level but only provide qualitative information at national level.

Adjusted jackknife for imputation under unequal probability sampling without replacement

Imputation is commonly used to compensate for item non-response in sample surveys. If we treat the imputed values as if they are true values, and then compute the variance estimates by using standard methods, such as the jackknife, we can seriously underestimate the true variances. We propose a modified jackknife variance estimator which is defined for any without-replacement unequal probability sampling design in the presence of imputation and non-negligible sampling fraction. Mean, ratio and random-imputation methods will be considered. The practical advantage of the method proposed is its breadth of applicability.