Assumption Of Random Sampling Research Articles

Skyline Fossilized Birth-Death Model is Robust to Violations of Sampling Assumptions in Total-Evidence Dating.

Several total-evidence dating studies under the fossilized birth-death (FBD) model have produced very old age estimates, which are not supported by the fossil record. This phenomenon has been termed "deep root attraction (DRA)." For two specific data sets, involving divergence time estimation for the early radiations of ants, bees, and wasps (Hymenoptera) and of placental mammals (Eutheria), it has been shown that the DRA effect can be greatly reduced by accommodating the fact that extant species in these trees have been sampled to maximize diversity, so-called diversified sampling. Unfortunately, current methods to accommodate diversified sampling only consider the extreme case where it is possible to identify a cut-off time such that all splits occurring before this time are represented in the sampled tree but none of the younger splits. In reality, the sampling bias is rarely this extreme and may be difficult to model properly. Similar modeling challenges apply to the sampling of the fossil record. This raises the question of whether it is possible to find dating methods that are more robust to sampling biases. Here, we show that the skyline FBD (SFBD) process, where the diversification and fossil-sampling rates can vary over time in a piecewise fashion, provides age estimates that are more robust to inadequacies in the modeling of the sampling process and less sensitive to DRA effects. In the SFBD model we consider, rates in different time intervals are either considered to be independent and identically distributed or assumed to be autocorrelated following an Ornstein-Uhlenbeck (OU) process. Through simulations and reanalyses of Hymenoptera and Eutheria data, we show that both variants of the SFBD model unify age estimates under random and diversified sampling assumptions. The SFBD model can resolve DRA by absorbing the deviations from the sampling assumptions into the inferred dynamics of the diversification process over time. Although this means that the inferred diversification dynamics must be interpreted with caution, taking sampling biases into account, we conclude that the SFBD model represents the most robust approach currently available for addressing DRA in total-evidence dating.

Incidence‐data‐based species richness estimation via a Beta‐Binomial model

Abstract Individual‐based abundance data and sample‐based incidence data are the two most widely used survey data formats to assess the species diversity in a target area, where the sample‐based incidence data are more available and efficient for estimating species richness. For species individual with spatial aggregation, individual‐unit‐based random sampling scheme is difficult to implement, and quadrat‐unit‐based sampling scheme is more available to implement and more likely to fit the model assumption of random sampling. In addition, sample‐based incidence data, without recording the number of individuals of a species and only recording the binary presence or absence of a species in the sampled unit, could considerably reduce the survey loading in the field. In this study, according to sample‐based incidence data and based on a beta‐binomial model assumption, instead of using the maximum likelihood method, I used the moment method to derive the richness estimator. The proposed richness estimation method provides a lower bound estimator of species richness for beta‐binomial models, in which the new method only uses the number of singletons, doubletons and tripletons in the sample to estimate undetected richness. I evaluated the proposed estimator using simulated datasets generated from various species abundance models. For highly heterogeneous communities, the simulation results indicate that the proposed estimator could provide a more stable, less biased estimate and a more accurate 95% confidence interval of true richness compared to other traditional parametric‐based estimators. I also applied the proposed approach to real datasets for assessment and comparison with traditional estimators. The newly proposed richness estimator provides different information and conclusions from other estimators.

On the descriptive value of the reliance on small-samples assumption

AbstractExperience is the best teacher. Yet, in the context of repeated decisions, experience was found to trigger deviations from maximization in the direction of underweighting of rare events. Evaluations of alternative explanations for this bias led to contradicting conclusions. Studies that focused on the aggregate choice rates, including a series of choice prediction competitions, favored the assumption that this bias reflects reliance on small samples. In contrast, studies that focused on individual decisions suggest that the bias reflects a strong myopic tendency by a significant minority of participants. The current analysis clarifies the apparent inconsistency by reanalyzing a data set that previously led to contradicting conclusions. Our analysis suggests that the apparent inconsistency reflects the differing focus of the cognitive models. Specifically, sequential adjustment models (that assume sensitivity to the payoffs’ weighted averages) tend to find support for the hypothesis that the deviations from maximization are a product of strong positive recency (a form of myopia). Conversely, models assuming random sampling of past experiences tend to find support to the hypothesis that the deviations reflect reliance on small samples. We propose that the debate should be resolved by using the assumptions that provide better predictions. Applying this solution to the data set we analyzed shows that the random sampling assumption outperforms the weighted average assumption both when predicting the aggregate choice rates and when predicting the individual decisions.

Population abundance in arctic grayling using genetics and close-kin mark-recapture.

Arctic Grayling (Thymallus arcticus) are among the most widely distributed and abundant freshwater fish in the Yukon Territory of Canada, yet little information exists regarding their broad and fine‐scale population structures or the number and size of these populations. The estimation of population abundance is fundamental for robust management and conservation, yet estimating abundance in the wild is often difficult. Here, we estimated abundance of an Arctic Grayling population using multiple genetic markers and the close‐kin mark‐recapture (CKMR) method. A total of N = 1,104 Arctic Grayling collected from two systems in Yukon were genotyped at 38 sequenced microsatellites. We first identified structure and assessed genetic diversity (effective population size, N^e). Collections from one of the systems (Lubbock River) comprised adults and young‐of‐the‐year sampled independently allowing the identification of parent–offspring pairs (POPs), and thus, the estimation of abundance using CKMR. We used COLONY and CKMRsim to identify POPs and both provided similar results leading to indistinguishable estimates (95% CI) of census size, that is, N^c(COLONY) = 1858 (1259–2457) and N^c(CKMRsim)=1812 (1229–2389). The accuracy of the population abundance estimates can in the future be improved with temporal sampling and more precise age or size‐specific fecundity estimates for Arctic Grayling. Our study demonstrates that the method can be used to inform management and conservation policy for Arctic Grayling and likely also for other fish species for which the assumption of random and independent sampling of adults and offspring can be assured.

Evaluation of glass evidence at activity level: A new distribution for the background population

For evidence evaluation of the physicochemical properties of glass at activity level a well-known formula introduced by Evett & Buckleton [1,2] is commonly used. Parameters in this formula are, amongst others, the probability in a background population to find on somebody’s clothing the observed number of glass sources and the probability in a background population to find on somebody’s clothing a group of fragments with the same size as the observed matching group.Recently, for efficiency reasons, the Netherlands Forensic Institute changed its methodology to measure not all the glass fragments but a subset of glass fragments found on clothing. Due to the measurement of subsets, it is difficult to get accurate estimates for these parameters in this formula.We offer a solution to this problem. The heart of the solution consists of relaxing the assumption of conditional independence of group sizes of background fragments, and modelling the probability of an allocation of background fragments into groups given a total number of background fragments by a two-parameter Chinese restaurant process (CRP) [3]. Under the assumption of random sampling of fragments to be measured from recovered fragments in the laboratory, parameter values for the Chinese restaurant process may be estimated from a relatively small dataset of glass in other relevant cases.We demonstrate this for a dataset of glass fragments collected from upper garments in casework, show model fit and provide a prototypical calculation of an LR at activity level accompanied with a parameter sensitivity analysis for reasonable ranges of the CRP parameter values.Considering that other laboratories may want to measure subsets as well, we believe this is an important alternative approach to the evaluation of numerical LRs for glass analyses at activity level.

Estimated Number of People who Inject Drugs in Ho Chi Minh City, Vietnam: Findings from a Two-survey Capture—Recapture Population Size Estimation Exercise

BackgroundHIV/AIDS program managers in Ho Chi Minh City (HCMC), Vietnam have always relied on the police reports and the UNAIDS Estimation and Projection Package for population size estimation of People Who Inject Drugs (PWID).MethodsWe used Respondent-driven Sampling (RDS) to implement a two-source capture—recapture study to estimate the population size of PWID in HCMC in 2017. The study was implemented in seven out of 24 districts and included men and women ages 18 years and older who reported injecting illicit drugs in the last 90 days, and who had lived in the city for the past six months. Estimates of the PWID population size for each of the seven districts were calculated accounting for the RDS sampling design. These were then adjusted to account for the district sampling probabilities to give an estimate for HCMC. Chapman two-source capture—recapture estimates of population size, based on simple random sampling assumptions, were also calculated for comparison.ResultsThe estimates resulted in a population size for HCMC of 19,155 [95% Confidence Interval (CI): 17,006–25,039] using the RDS approach and 17,947 (95% CI: 15,968–19,928), using the Chapman approach.ConclusionThe two-survey capture—recapture exercise provided estimates of PWID in HCMC — based on Chapman estimator and RDS approach — are similar. For planning HIV prevention and care service needs among PWID in HCMC, both estimates may need to be taken into consideration together with size estimates from other sources.

Validation of close‐kin mark–recapture (CKMR) methods for estimating population abundance

Abstract Knowing how many individuals there are in a population is a fundamental problem in the management and conservation of freshwater and marine fish. We compare abundance estimates (census size, Nc) in seven brook trout Salvelinus fontinalis populations using standard mark–recapture (MR) and the close‐kin mark–recapture (CKMR) method. Our purpose is to validate CKMR as a method for estimating population size. Close‐kin mark–recapture is based on the principle that an individual's genotype can be considered a “recapture” of the genotypes of each of its parents. Assuming offspring and parents are sampled independently, the number of parent–offspring pairs (POPs) genetically identified in these samples can be used to estimate abundance. We genotyped (33 microsatellites) and aged c. 2,400 brook trout individuals collected over 5 consecutive years (2014–2018). We provide an alternative interpretation of CKMR in terms of the Lincoln–Petersen estimator in which the parents are considered as tagging the offspring rather than the offspring “recapturing” the parents. Despite various sources of uncertainty, we find close agreement between standard MR abundance estimates obtained through double‐pass electrofishing and CKMR estimates, which require information on age‐specific fecundity, and population‐ and age‐specific survival rates. Population sizes () are estimated to range between 300 and 6,000 adult individuals. Our study constitutes the first in situ validation of CKMR and establishes it as a useful method for estimating population size in aquatic systems where assumptions of random sampling and thorough mixing of individuals can be met.

A randomization test wrapper for synthesizing single-case experiments using multilevel models: A Monte Carlo simulation study.

Multilevel models (MLMs) have been proposed in single-case research, to synthesize data from a group of cases in a multiple-baseline design (MBD). A limitation of this approach is that MLMs require several statistical assumptions that are often violated in single-case research. In this article we propose a solution to this limitation by presenting a randomization test (RT) wrapper for MLMs that offers a nonparametric way to evaluate treatment effects, without making distributional assumptions or an assumption of random sampling. We present the rationale underlying the proposed technique and validate its performance (with respect to Type I error rate and power) as compared to parametric statistical inference in MLMs, in the context of evaluating the average treatment effect across cases in an MBD. We performed a simulation study that manipulated the numbers of cases and of observations per case in a dataset, the data variability between cases, the distributional characteristics of the data, the level of autocorrelation, and the size of the treatment effect in the data. The results showed that the power of the RT wrapper is superior to the power of parametric tests based on F distributions for MBDs with fewer than five cases, and that the Type I error rate of the RT wrapper is controlled for bimodal data, whereas this is not the case for traditional MLMs.

Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions

Abstract. Groundwater travel time distributions (TTDs) provide a robust description of the subsurface mixing behavior and hydrological response of a subsurface system. Lagrangian particle tracking is often used to derive the groundwater TTDs. The reliability of this approach is subjected to the uncertainty of external forcings, internal hydraulic properties, and the interplay between them. Here, we evaluate the uncertainty of catchment groundwater TTDs in an agricultural catchment using a 3-D groundwater model with an overall focus on revealing the relationship between external forcing, internal hydraulic properties, and TTD predictions. Eight recharge realizations are sampled from a high-resolution dataset of land surface fluxes and states. Calibration-constrained hydraulic conductivity fields (Ks fields) are stochastically generated using the null-space Monte Carlo (NSMC) method for each recharge realization. The random walk particle tracking (RWPT) method is used to track the pathways of particles and compute travel times. Moreover, an analytical model under the random sampling (RS) assumption is fit against the numerical solutions, serving as a reference for the mixing behavior of the model domain. The StorAge Selection (SAS) function is used to interpret the results in terms of quantifying the systematic preference for discharging young/old water. The simulation results reveal the primary effect of recharge on the predicted mean travel time (MTT). The different realizations of calibration-constrained Ks fields moderately magnify or attenuate the predicted MTTs. The analytical model does not properly replicate the numerical solution, and it underestimates the mean travel time. Simulated SAS functions indicate an overall preference for young water for all realizations. The spatial pattern of recharge controls the shape and breadth of simulated TTDs and SAS functions by changing the spatial distribution of particles' pathways. In conclusion, overlooking the spatial nonuniformity and uncertainty of input (forcing) will result in biased travel time predictions. We also highlight the worth of reliable observations in reducing predictive uncertainty and the good interpretability of SAS functions in terms of understanding catchment transport processes.

Chimpanzees Consider Humans’ Psychological States when Drawing Statistical Inferences

Great apes have been shown to be intuitive statisticians: they can use proportional information within a population to make intuitive probability judgments about randomly drawn samples [1,J.E., J.C., J.H., E.H., and H.R., unpublished data]. Humans, from early infancy onward, functionally integrate intuitive statistics with other cognitive domains to judge the randomness of an event [2-6]. To date, nothing is known about such cross-domain integration in any nonhuman animal, leaving uncertainty about the origins of human statistical abilities. We investigated whether chimpanzees take into account information about psychological states of experimenters (their biases and visual access) when drawing statistical inferences. We tested 21 sanctuary-living chimpanzees in a previously established paradigm that required subjects to infer which of two mixed populations of preferred and non-preferred food items was more likely to lead to a desired outcome for the subject. In a series of three experiments, we found that chimpanzees chose based on proportional information alone when they had no information about experimenters' preferences and (to a lesser extent) when experimenters had biases for certain food types but drew blindly. By contrast, when biased experimenters had visual access, subjects ignored statistical information and instead chose based on experimenters' biases. Lastly, chimpanzees intuitively used a violation of statistical likelihoods as indication for biased sampling. Our results suggest that chimpanzees have a random sampling assumption that can be overridden under the appropriate circumstances and that they are able to use mental state information to judge whether this is necessary. This provides further evidence for a shared statistical inference mechanism in apes and humans.

Correction for bias in meta-analysis of little-replicated studies.

Meta‐analyses conventionally weight study estimates on the inverse of their error variance, in order to maximize precision. Unbiased variability in the estimates of these study‐level error variances increases with the inverse of study‐level replication. Here, we demonstrate how this variability accumulates asymmetrically across studies in precision‐weighted meta‐analysis, to cause undervaluation of the meta‐level effect size or its error variance (the meta‐effect and meta‐variance).Small samples, typical of the ecological literature, induce big sampling errors in variance estimation, which substantially bias precision‐weighted meta‐analysis. Simulations revealed that biases differed little between random‐ and fixed‐effects tests. Meta‐estimation of a one‐sample mean from 20 studies, with sample sizes of 3–20 observations, undervalued the meta‐variance by c. 20%. Meta‐analysis of two‐sample designs from 20 studies, with sample sizes of 3–10 observations, undervalued the meta‐variance by 15%–20% for the log response ratio (lnR); it undervalued the meta‐effect by c. 10% for the standardized mean difference (SMD).For all estimators, biases were eliminated or reduced by a simple adjustment to the weighting on study precision. The study‐specific component of error variance prone to sampling error and not parametrically attributable to study‐specific replication was replaced by its cross‐study mean, on the assumptions of random sampling from the same population variance for all studies, and sufficient studies for averaging. Weighting each study by the inverse of this mean‐adjusted error variance universally improved accuracy in estimation of both the meta‐effect and its significance, regardless of number of studies. For comparison, weighting only on sample size gave the same improvement in accuracy, but could not sensibly estimate significance.For the one‐sample mean and two‐sample lnR, adjusted weighting also improved estimation of between‐study variance by DerSimonian‐Laird and REML methods. For random‐effects meta‐analysis of SMD from little‐replicated studies, the most accurate meta‐estimates obtained from adjusted weights following conventionally weighted estimation of between‐study variance.We recommend adoption of weighting by inverse adjusted‐variance for meta‐analyses of well‐ and little‐replicated studies, because it improves accuracy and significance of meta‐estimates, and it can extend the scope of the meta‐analysis to include some studies without variance estimates.

Tdap Vaccination Among Healthcare Personnel, Internet Panel Survey, 2012–2014

Healthcare personnel (HCP) are at risk for pertussis infection exposure or transmitting the disease to patients in their work settings. The Advisory Committee on Immunization Practices recommends tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis (Tdap) vaccination for HCP to minimize these risks. This study assessed Tdap vaccination coverage among U.S. HCP by sociodemographic and occupation-related characteristics. The 2012, 2013, and 2014 Internet Panel Surveys were analyzed in 2015 to assess HCP Tdap vaccination. Effective sample sizes for 2012, 2013, and 2014 survey years were 2,038, 1613, and 1633, respectively. Missing values were assigned using multiple imputation. Multivariable logistic regression identified factors independently associated with HCP Tdap vaccination. Statistical measures were calculated with an assumption of random sampling. Overall, Tdap vaccination coverage among HCP was 34.8% (95% CI=30.6%, 39.0%); 40.2% (95% CI=36.1%, 44.4%); and 42.4% (95% CI=38.7%, 46.0%) in 2012, 2013, and 2014, respectively. Nurse practitioners/physician's assistants, physicians, nurses, and HCP working in hospitals and ambulatory care settings had higher Tdap coverage. Having contact with an infant aged ≤6 months and influenza vaccination receipt were associated with increased Tdap vaccination. Non-Hispanic black race/ethnicity, having an associate/bachelor's degree, being below poverty, non-clinical personnel status, and working in a long-term care setting were associated with decreased Tdap vaccination. HCP Tdap vaccination coverage increased during 2012-2014; however, coverage remains low. Vaccination coverage varied widely by healthcare occupation, occupational setting, and sociodemographic characteristics. Evidence-based employer strategies used to increase HCP influenza vaccination, if applied to Tdap, may increase Tdap coverage.

The no miracles argument and the base rate fallacy

The no miracles argument is one of the main arguments for scientific realism. Recently it has been alleged that the no miracles argument is fundamentally flawed because it commits the base rate fallacy. The allegation is based on the idea that the appeal of the no miracles argument arises from inappropriate neglect of the base rate of approximate truth among the relevant population of theories. However, the base rate fallacy allegation relies on an assumption of random sampling of individuals from the population which cannot be made in the case of the no miracles argument. Therefore the base rate fallacy objection to the no miracles argument fails. I distinguish between a “local” and a “global” form of the no miracles argument. The base rate fallacy objection has been leveled at the local version. I argue that the global argument plays a key role in supporting a base-rate-fallacy-free formulation of the local version of the argument.

A two-step semiparametric method to accommodate sampling weights in multiple imputation.

Multiple imputation (MI) is a well-established method to handle item-nonresponse in sample surveys. Survey data obtained from complex sampling designs often involve features that include unequal probability of selection. MI requires imputation to be congenial, that is, for the imputations to come from a Bayesian predictive distribution and for the observed and complete data estimator to equal the posterior mean given the observed or complete data, and similarly for the observed and complete variance estimator to equal the posterior variance given the observed or complete data; more colloquially, the analyst and imputer make similar modeling assumptions. Yet multiply imputed data sets from complex sample designs with unequal sampling weights are typically imputed under simple random sampling assumptions and then analyzed using methods that account for the sampling weights. This is a setting in which the analyst assumes more than the imputer, which can led to biased estimates and anti-conservative inference. Less commonly used alternatives such as including case weights as predictors in the imputation model typically require interaction terms for more complex estimators such as regression coefficients, and can be vulnerable to model misspecification and difficult to implement. We develop a simple two-step MI framework that accounts for sampling weights using a weighted finite population Bayesian bootstrap method to validly impute the whole population (including item nonresponse) from the observed data. In the second step, having generated posterior predictive distributions of the entire population, we use standard IID imputation to handle the item nonresponse. Simulation results show that the proposed method has good frequentist properties and is robust to model misspecification compared to alternative approaches. We apply the proposed method to accommodate missing data in the Behavioral Risk Factor Surveillance System when estimating means and parameters of regression models.

RRTCS: An R Package for Randomized Response Techniques in Complex Surveys.

Randomized response (RR) techniques may be used to compile more reliable data, to protect the respondent's confidentiality, and to avoid an unacceptable rate of nonresponse when the information requested is sensitive (e.g., concerning racism, drug use, abortion, delinquency, AIDS, or academic cheating). Standard RR methods are used primarily in surveys that require a binary response to a sensitive question, and seek to estimate the proportion of people presenting a given (sensitive) characteristic. Nevertheless, some studies have addressed situations in which the response to a sensitive question results in a quantitative variable. RR methods are usually developed assuming that the sample is obtained using simple random sampling. However, in practice, most surveys are complex and involve stratification, clustering, and an unequal probability of selection of the sample. Data from complex survey designs require special consideration with regard to the estimation of finite population parameters and to the corresponding variance estimation procedures, due to the reality of significant departures from the simple random sampling assumption. In such a complex survey design, unbiased variance estimation is not easy to calculate, because of clustering and the involvement of (generally complex) second-order inclusion probabilities. In view of these considerations, a new computer program has been developed to provide a method for estimating the parameters of sensitive characteristics under a variety of complex sampling designs.

Frames2: A Package for Estimation in Dual Frame Surveys

Data from complex survey designs require special consideration with regard to estimation of finite population parameters and corresponding variance estimation procedures, as a consequence of significant departures from the simple random sampling assumption. In the past decade a number of statistical software packages have been developed to facilitate the analysis of complex survey data. All these statistical software packages are able to treat samples selected from one sampling frame containing all population units. Dual frame surveys are very useful when it is not possible to guarantee a complete coverage of the target population and may result in considerable cost savings over a single frame design with comparable precision. There are several estimators available in the statistical literature but no existing software covers dual frame estimation procedures. This gap is now filled by package Frames2. In this paper we highlight the main features of the package. The package includes the main estimators in dual frame surveys and also provides interval confidence estimation. In practice, the assumption that the sampling frame contains all population units is rarely met. Often, one finds that sampling from a frame which is known to cover approximately all units in the population is quite expensive while other frames (e.g. special lists of units) are available for cheaper sampling methods. However, the latter usually only cover an unknown or only approximately known fraction of the population. A common example of frame undercoverage is provided by telephone surveys. Estimation could be affected by serious bias due to the lack of a telephone in some households and the generalised use of mobile phones, which are sometimes replacing fixed (land) lines entirely. The potential for coverage error as a result of the exponential growth of the cell-phone only population has led to the development of dual-frame surveys. In these designs, a traditional sample from the landline frame is supplemented with an independent sample from a register of cell-phone numbers. The dual frame sampling approach assumes that two frames are available for sampling and that, overall, they cover the entire target population. The most common situation is the one represented in Figure 1 where the two frames, say frame A and frame B, show a certain degree of overlapping, so it is possible to distinguish three disjoint non-empty domains: domain a, containing units belonging to frame A but not to frame B; domain b, containing units belonging to frame B but not to frame A and domain ab, containing units belonging to both frames. As an example, consider a telephone survey where both landline and cell phone lists are available; let A be the landline frame and B the cell phone frame. Then, it is possible to distinguish three types of individuals: landline only units, cell-only units and units with both landline and cell phone, which will compose domain a, b and ab, respectively. Nevertheless, one can face some other situations depending on the relative positions of the frames. For example, Figure 2 shows the case in which frame B is totally included in frame A, that is, frame B is a subset of frame A. Here domain b is empty. We also may find scenarios where the two sampling frames exactly match, as depicted in Figure 3, where ab is the only non-empty domain. Finally, the scenario where domain ab is empty has no interest from a dual frame perspective, since it can be

Average-case linear-time similar substring searching by the q-gram distance

In this paper we consider the problem of similar substring searching in the q-gram distance. The q-gram distance dq(x,y) is a similarity measure between two strings x and y defined by the number of different q-grams between them. The distance can be used instead of the edit distance due to its lower computation cost, O(|x|+|y|) vs. O(|x||y|), and its good approximation for the edit distance. However, if this distance is applied to the problem of finding all similar strings, in a long text t, to a given pattern p, the total computation cost is sometimes not acceptable. Ukkonen already proposed two fast algorithms: one with an array and the other with a tree. When “similar” means k or less in dq, their time complexities are O(|t|k+|p|) and O(|t|logk+|p|), respectively. In this paper, we propose two algorithms of average-case complexity O(|t|+|p|), although their worst-case complexities are still O(|t|k+|p|) and O(|t|logk+|p|), respectively. The linearity of the average-case complexity is analyzed under the assumption of random sampling of t and the condition that q is larger than a threshold. The algorithms exploit the fact that similar substrings in t are often found at very close positions if the beginning positions of the substrings are close. In the second proposed algorithm, we adopted a doubly-linked list supported by an array and a search tree to search for a list element in O(logk) time. Experimental results support their theoretical average-case complexities.

Age-dependent estimates of the epidemiological impact of pandemic influenza (H1N1-2009) in Japan.

The total number of influenza cases with medical attendance has been estimated from sentinel surveillance data in Japan under a random sampling assumption of sentinel medical institutions among the total medical institutions. The 2009 pandemic offered a research opportunity to validate the sentinel-based estimation method using the estimated proportion of infections measured by the population-wide seroepidemiological survey employing hemagglutinin inhibition (HI) assay. For the entire population, we estimated the age-standardized proportion of infections at 28.5% and 23.5% using cut-off values of HI titer at 1 : 20 and 1 : 40, respectively. Investigating the age profiles, we show that the estimated influenza-like illness (ILI) cases with medical attendance exceeded the estimated infections among those aged from 0 to 19 years, indicating an overestimation of the magnitude by sentinel-based estimation method. The ratio of estimated cases to estimated infections decreased as a function of age. Examining the geographic distributions, no positive correlation was identified between the estimated cases and infections. Our findings indicate a serious technical limitation of the so-called multiplier method in appropriately quantifying the risk of influenza due to limited specificity of ILI and reporting bias. A seroepidemiological study should be planned in advance of a pandemic.

Likelihood analysis of species occurrence probability from presence‐only data for modelling species distributions

Summary1. Understanding the factors affecting species occurrence is a pre‐eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so‐called presence‐only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. maxent is a widely used software program designed to model and map species distribution using presence‐only data.2. We provide a critical review of maxent as applied to species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that maxent produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of occurrence (ψ). This focus on an index was motivated by the belief that it is not possible to estimate ψ from presence‐only data; however, we demonstrate that ψ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of species detection.3. The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that maxent produces extreme under‐predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that maxent predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the maxent method.4. As with maxent, formal model‐based inference requires a random sample of presence locations. Many presence‐only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.

Detecting Treatment Effects with Small Samples: The Power of Some Tests Under the Randomization Model

Randomization tests are often recommended when parametric assumptions may be violated because they require no distributional or random sampling assumptions in order to be valid. In addition to being exact, a randomization test may also be more powerful than its parametric counterpart. This was demonstrated in a simulation study which examined the conditional power of three nondirectional tests: the randomization t test, the Wilcoxon–Mann–Whitney (WMW) test, and the parametric t test. When the treatment effect was skewed, with degree of skewness correlated with the size of the effect, the randomization t test was systematically more powerful than the parametric t test. The relative power of the WMW test under the skewed treatment effect condition depended on the sample size ratio.