Separate Estimates Research Articles

A Model-Dependent Method for Monitoring Subtle Changes in Vegetation Height in the Boreal–Alpine Ecotone Using Bi-Temporal, Three Dimensional Point Data from Airborne Laser Scanning

The boreal tree line is in many places expected to advance upwards into the mountains due to climate change. This study aimed to develop a general method for estimation of vegetation height change in general, and change in tree height more specifically, for small geographical domains utilizing bi-temporal airborne laser scanner (ALS) data. The domains subject to estimation may subsequently be used to monitor vegetation and tree height change with detailed temporal and geographical resolutions. A method was developed with particular focus on statistically rigorous estimators of uncertainty for change estimates. The method employed model-dependent statistical inference. The method was demonstrated in a 12 ha study site in a boreal–alpine tree line in southeastern Norway, in which 316 trees were measured on the ground in 2006 and 2012 and ALS data were acquired in two temporally coincident campaigns. The trees ranged from 0.11 m to 5.20 m in height. Average growth in height was 0.19 m. Regression models were used to predict and estimate change. By following the area-based approach, predictions were produced for every individual 2 m2 population element that tessellated the study area. Two demonstrations of the method are provided in which separate height change estimates were calculated for domains of size 1.5 ha or greater. Differences in height change estimates among such small domains illustrate how change patterns may vary over the landscape. Model-dependent mean square error estimates for the height change estimators that accounted for (1) model parameter uncertainty, (2) residual variance, and (3) residual covariance are provided. Findings suggested that the two latter sources of uncertainty could be ignored in the uncertainty analysis. The proposed estimators are likely to work well for estimation of differences in height change along a gradient of small monitoring units, like the 1.5 ha cells used for demonstration purposes, and thus may potentially be used to monitor tree line migration over time.

Delayed Output Feedback Control for Gait Assistance With a Robotic Hip Exoskeleton

In this paper, we propose a new and simple control strategy for gait assistance with a hip exoskeleton robot. This controller is based on the time delayed, self-feedback known for stabilizing oscillatory systems under certain conditions. In this controller, there are no separate estimators for the gait phase nor the environment, yet the controller can be generalized to operate under various walking conditions (e.g., stair and ramp walking). We first define a state variable representing the current leg's movement with hip joint angles. A simple assistance control can be described in closed-loop form with the delayed state feedback. By assigning the appropriate time-delay and self-feedback gain, we can generate assistive torques stably under the interaction between human and exoskeleton. The controller provides immediate and smooth assistance to user movement by reflecting the change of leg motion at every control period. The proposed joint-angle-based delayed-feedback assistance controller can operate under various walking speeds and environmental changes (e.g., stairs and ramps) without the need for additional sensors, computational processing, and parameter adjustment. Using a simple leg swing model, we perform a stability analysis under a simplified condition to provide insights into the effects of the time-delayed feedback in oscillatory systems. Then, we experimentally validate the efficacy of the proposed assistance controller by measuring the metabolic energy expenditure for level treadmill walking. We also test and analyze the generated assistive torques and power under the different walking conditions to show the generalizability of the controller.

Monitoring snowpack outflow volumes and their isotopic composition to better understand streamflow generation during rain-on-snow events

Abstract. Rain-on-snow (ROS) events in mountainous catchments can cause enhanced snowmelt, leading to an increased risk of destructive winter floods. However, due to differences in topography and forest cover, the generation of snowpack outflow volumes and their contribution to streamflow are spatially and temporally variable during ROS events. In order to adequately predict such flood events with hydrological models, an enhanced process understanding of the contribution of rainwater and snowmelt to stream water is needed. In this study, we monitored and sampled snowpack outflow with fully automated snowmelt lysimeter systems installed at three different elevations in a pre-Alpine catchment in central Switzerland. We measured snowpack outflow volumes during the winters of 2017 and 2018, as well as snowpack outflow isotopic compositions in winter 2017. Snowpack outflow volumes were highly variable in time and space, reflecting differences in snow accumulation and melt. In winter 2017, around 815 mm of snowpack outflow occurred at our reference site (grassland 1220 m a.s.l. – metres above sea level), whereas snowpack outflow was 16 % less at the nearby forest site (1185 m a.s.l.), and 62 % greater at another grassland site located 200 m higher (1420 m a.s.l.). A detailed analysis of 10 ROS events showed that the differences in snowpack outflow volumes could be explained mainly by rainfall volumes and initial snow depths. The isotope signals of snowpack outflow were more damped than those of incoming rainwater at all three sites, with the most damped signal at the highest elevation site because its snowpack was the thickest and the residence times of liquid water in its snowpack were the longest, thus enhancing isotopic mixing in the snowpack. The contribution of snowpack outflow to streamflow, estimated with an isotope-based two-component end-member mixing model, differed substantially among the three lysimeter sites (i.e. between 7±4 and 91±21 %). Because the vegetation in our study catchment is a mixture of grassland and forest, with elevations ranging from 1000 to 1500 m a.s.l., our site-specific hydrograph separation estimates can only provide a range of snowpack outflow contributions to discharge from different parts of the study area. Thus, the catchment-average contribution of snowpack outflow to stream discharge is likely to lie between the end-member mixing estimates derived from the three site-specific data sets. This information may be useful for improving hydrological models in snow-dominated catchments.

Education Spillovers in Farm Productivity: Revisiting the Evidence

Abstract This paper exploits the social organization of India to revisit the question of education spillovers in farm productivity. The fact that social interactions mainly occur within castes in rural India provides tools to show that the observed correlation between farm productivity and neighbors’ education is likely to be a spillover effect. In particular, there are no cross-caste and no cross-occupation effects, which underlines that, under specific assumptions, which are stated and explored in the paper, the education of neighbors does not capture the effect of group unobservables. This evidence is complemented by separate estimations by crops, which show results that are consistent with education spillovers. The strategy used in this paper helps understand and interpret previous findings from the literature.

Prevalence of Medication Errors Among Paediatric Inpatients: Systematic Review and Meta-Analysis.

The risk of medication errors is high in paediatric inpatient settings. However, estimates of the prevalence of medication errors have not accounted for heterogeneity across studies in error identification methods and definitions, nor contextual differences across wards and the use of electronic or paper medication charts. Our aim was to conduct a systematic review and meta-analysis to provide separate estimates of the prevalence of medication errors among paediatric inpatients, depending on hospital ward and the use of electronic or paper medication charts, that address differences in error identification methods and definitions. We systematically searched five databases to identify studies published between January 2000 and December 2018 that assessed medication error rates by medication chart audit, direct observation or a combination of methods. We identified 71 studies, 19 involved paediatric wards using electronic charts. Most studies assessed prescribing errors with few studies assessing administration errors. Estimates varied by ward type. Studies of paediatric wards using electronic charts generally reported a reduced error prevalence compared to those using paper, although there were some inconsistencies. Error detection methods impacted the rate of administration errors in studies of multiple wards, however, no other difference was found. Definition of medication error did not have a consistent impact on reported error rates. Medication errors are a frequent occurrence in paediatric inpatient settings, particularly in intensive care wards and emergency departments. Hospitals using electronic charts tended to have a lower rate of medication errors compared to those using paper charts. Future research employing controlled designs is needed to determine the true impact of electronic charts and other interventions on medication errors and associated harm among hospitalized children.

Modified Ratio Estimator of Finite Population Total in Stratified Random Sampling Under Non-Response

In this paper, we propose a separate ratio-type estimator of finite population total. The procedure of subsampling non-respondents suggested by Hansen and Hurwitz (1946) is considered. Asymptotic properties of the proposed estimator are studied under stratified random sampling. Study of the asymptotic properties shows that the suggested estimator is unbiased and consistent. We recommend that simulation study should be done to check for performance of the estimator. KeyWords : Stratified Random Sampling, Ratio Estimation, Non-Response DOI : 10.7176/MTM/9-7-08 Publication date : July 31 st 2019

Separable Estimation of Ambient Noise Spectrum in Synchrophasor Measurements in the Presence of Forced Oscillations

The ambient noise in synchrophasor measurements carries core dynamic signatures of a power system and its spectrum can strongly reflect the dynamic properties and operating conditions of the system and its loads. Typically, the ambient noise spectrum is estimated from a window of measurements captured during steady state operating conditions of the system. The presence of any Forced Oscillation (FO) in the measurement window severely biases the estimated ambient noise spectrum. In this article, an algorithm is proposed for separable estimation of the ambient noise spectrum in the presence of periodic FOs in synchrophasor measurements. The algorithm utilizes the Thomson's multitaper spectral estimation and harmonic analysis techniques and is capable of effectively reducing the bias in estimating the ambient noise spectrum due to the presence of FOs in the measurements. The performance of the estimator is analyzed theoretically and the theoretical results are verified experimentally using simulation studies. Application of the algorithm to field measured data illustrates that the algorithm is capable of effectively reducing the bias introduced by FOs in estimating the ambient noise spectrum from the synchrophasor measurements containing both ambient noise and FOs.

Some practical aspects of predicting texture data in digital soil mapping

Soil texture is the most well-known composition in soil science. When separate components of the texture (sand, silt, and clay) are predicted independently in digital soil mapping (DSM), there is no guarantee that the separate estimates will sum to 100%. Log-ratio transformations before DSM modelling are alternatives to guarantee a constant sum of the estimates. Little is known about the effect of non-summing to 100% and transformations of particle-size fractions (PSFs) when DSM products were used to predict soil functional properties using pedotransfer functions (PTFs). Therefore, this study was conducted to investigate the effect of different soil texture modelling methods on the estimation of available soil water capacity (AWC) and the total amount of irrigation water (TIW) required for wheat production on a 4600 ha area in Khuzestan province, southwestern Iran. Specifically, this study aimed i) to assess the performance of random forest models (RF) to predict untransformed (UT) and transformed PSFs using environmental covariates; ii) to study the effects of three widely used log-ratio transformations including additive, centroid and isometric log-ratio transformations (alr, clr, and ilr respectively) on the estimations of AWC and TIW. A total of 150 soil samples were collected from the surface layers (0–30 cm) based on the conditioned Latin hypercube sampling (cLHS) procedure. Results indicated that, in terms of root mean square error (RMSE), RF provided similar accuracies in predicting PSFs for both untransformed and transformed data. However, transformation resulted in biased estimates. In addition, RF prediction based on untransformed data resulted in more correctly soil texture classes allocation when compared to transformed data. The spatial distribution of the sum of the predicted untransformed fractions indicated only small parts of the area conformed to the 100% sum. Almost the same accuracies for estimates of AWC were obtained when both untransformed and transformed predicted texture components were used as the inputs to PTFs. Data transformation can result in biased estimates of AWC. The findings indicated no significant difference between transformation methods in predicting AWC and TIW. The general patterns of the spatial distribution of the predicted AWCs across the whole area were the same for transformed and untransformed data (except for clr transformation).

Generalized Classes of Regression-Cum-Ratio Estimators of Population Mean in Stratified Random Sampling

In this paper, classes of separate and combined regression-cum-ratio estimators have been proposed for estimating the finite population mean in stratified random sampling. The expressions for biases and mean square errors (MSEs) of the proposed classes have been derived to the first order of approximation. It has also been verified that the proposed classes of estimators, at their optimum conditions, are equivalent to the separate regression estimator. The proposed classes of estimators have been compared with the other existing estimators using MSE criterion, and the conditions under which the proposed classes perform better have been obtained. Numerical illustrations are given in support of theoretical findings. Relevance of the work The estimation theory is relevant to various interdisciplinary areas of research including economics, clinical trials, population studies, engineering, agriculture, etc. Also, the problem of estimation of mean is of huge importance in research, for instance, the estimation of: average agricultural production, average life span of persons in a region, mean concentration of dissolved minerals in water, and much more. For the estimation of mean, several design-based approaches are being widely used, for instance, simple random sampling, stratified random sampling, two-phase sampling, etc. If the population under study is homogeneous, then the simple random sampling design is used at the estimation stage. However, in various practical situations, the research study is based on the heterogeneous population, and in that case the stratified random sampling procedure is preferable over the simple random sampling. Considering the above fact, an attempt has been made in this paper to develop the classes of generalized estimators for the mean of the variable under study using stratified random sampling.

Contributions of Stereopsis and Aviation Experience to Simulated Rotary Wing Altitude Estimation.

We examined the contribution of binocular vision and experience to performance on a simulated helicopter flight task. Although there is a long history of research on the role of binocular vision and stereopsis in aviation, there is no consensus on its operational relevance. This work addresses this using a naturalistic task in a virtual environment. Four high-resolution stereoscopic terrain types were viewed monocularly and binocularly. In separate experiments, we evaluated performance of undergraduate students and military aircrew on a simulated low hover altitude judgment task. Observers were asked to judge the distance between a virtual helicopter skid and the ground plane. Our results show that for both groups, altitude judgments are more accurate in the binocular viewing condition than in the monocular condition. However, in the monocular condition, aircrew were more accurate than undergraduate observers in estimating height of the skid above the ground. At simulated altitudes of 5 ft (1.5 m) or less, binocular vision provides a significant advantage for estimation of the depth separation between the landing skid and the ground, regardless of relevant operational experience. However, when binocular cues are unavailable aircrew outperform undergraduate observers, a result that likely reflects the impact of training on the ability to interpret monocular depth cues.

Single-channel source separation and parameters estimation of multi-component BPSK/QPSK signal based on 3-D EVR spectrum and wavelet analysis

This paper proposes an approach of single-channel source separation and parameter estimation of multi-component rectangular pulse-shaped BPSK/QPSK (binary/quadrature phase-shift keying) signal. By structuring multi-dimensional matrix from the observed signal, we may then apply 3-D EVR spectrum to determine the delay time and symbol period and therefore to estimate the basic waveform. A cost function is designed to estimate the carrier frequency and initial phase, and the periodic ambiguity problem is solved through wavelet analysis to get accurate symbol period. Finally, the component signal can be reconstructed with estimated parameters, and the separation stops adaptively by the SNR value which is estimated with the method of subspace-based decomposition. Simulation results confirmed the effectiveness of the proposed method.

Regime Detection Based Risk Modeling of Asset Classes

In this work, we have found a risk model that improves the performance of Risk Targeting. Risk Targeting in portfolio construction is implemented to improve capital utilization in growing markets and systematically step away from risk scenarios. However, the performance of risk targeting varies with different implementations of risk estimation. Risk Targeting using recent backward volatility estimates is the most popular risk targeting mechanism but it could not have anticipated a deep crisis like 2008 and it would hurt in range bound situations like the February 2018 drop and bounce back or the December 2018 drop and January 2019 bounce back. The drawbacks of recent volatility are that in such a risk model, short-term volatility is being used to detect a long-term risk event with and short term mean reversion effects are ignored .In this work, we will try to separate risk estimation into two risk models, a long-term risk model that predicts extreme risk scenarios based on macroeconomic data and a short-term risk model that adjusts risk based on short-term mean reversion effects. We then combine the output of the two risk models into a risk measure that enables a risk targeted allocation strategy to outperform static allocation in both crisis periods like 2008 and mean-reverting periods like 2018.Note that risk for a real-money investor is not an expectation of volatility but a measure of the probability of losing money if one is allocated to that asset class. Hence, we try to forecast a risk value which, if interpreted as a probability of loss, outperforms a baseline estimate of risk.

Dynamic Features of Placebo Effects Addressing Primary Insomnia

Background: It has been accepted knowledge that placebo effects have been significant in insomnia randomized clinical trials. However, the dynamic features of placebo effects have not been clarified. Our aim was therefore to conduct a meta-analysis of placebo-controlled randomized clinical trials to characterize the dynamic features of placebo effects addressing primary insomnia. Methods: We performed a comprehensive literature search for randomized, placebo-controlled, double-blind clinical trials evaluating the efficacy of therapeutic regimens addressing primary insomnia. We pooled separate effect size estimates (Hedge's g) of placebo and regimen conditions across trials for outcome measures, and multilevel mixed-effects models was used to explore potential sources of heterogeneity. Findings: The placebo effects were significant and robust to reduce the symptoms of insomnia, and subjective measures were significantly smaller than objective measures (p<0.001), but placebo response rates were nearly identical. The overall placebo effects were influenced by publication year (p = 0.015), treatment duration (p = 0.010), sample size (p<0.001), and therapeutic regimen (p<0.001). Placebo effects showed a diphasic feature within treatment duration: initial short-term increase-to-decrease status, and subsequent long-term slow increase-to-steady status. After withdrawals, placebo effects showed a sustained decline with fast-to-slow trend. In a large-scale time span, placebo effects of a new therapeutic regimen showed a slow-decline status. Interpretation: The dynamic features of placebo effects addressing primary insomnia may lead to the development and validation of dosing strategies that require less medication exposure to maintain clinical effects. Funding Statement: The authors state: There was no funding source for this study. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was followed for this pooled analysis.

Learning organizational ambidexterity

PurposeThe purpose of this paper is to reexamine exploration-exploitation’s reciprocality in organizational ambidexterity (OA) research. OA figures prominently in a variety of organization science phenomena. Introduced as a two-stage model for innovation, theory specifies reciprocal reinforcement between the OA processes of exploration (eR) and exploitation (eT). In this study, the authors argue that previous analyses of OA necessarily neglect this reciprocality in favor of conceptualizations that conform to common statistical techniques.Design/methodology/approachThe authors propose joint-variance (JV) as a soluble estimator of exploration–exploitation (eR-eT) reciprocality. An updated systematic literature synthesis yielded K = 50 studies (53 independent samples, N = 11,743) for further testing.FindingsThree primary findings are as follows: JV reduced negative confounding, explaining 45 per cent of between-study variance. JV quantified the positive confounding in separate meta-analytic estimates of eR and eT on performance because of double-counting (37.6 per cent), and substantive application of JV to hypothesis testing supported OA theoretical predictions.Research limitations/implicationsThe authors discuss practical consideration for eR-eT reciprocality, as well as theoretical contributions for cohering the OA empirical literature.Practical implicationsThe authors discuss design limitations and JV measurement extensions for the future.Social implicationsLearning in OA literature has been neglected or underestimated.Originality/valueBecause reciprocality is theorized, yet absent in current models, existing results represent confounded or biased evidence of the OA’s effect on firm performance. Subsequently, the authors propose JV as a soluble estimator of eR-eT learning modes.

Relative source location using coda-wave interferometry: Method, code package, and application to mining-induced earthquakes

A wide range of applications requires the relative locations of sources of energy to be known accurately. Most conventional location methods are either subject to errors that depend strongly on inaccuracy in the model of propagation velocity used or demand a well-distributed network of surrounding seismic stations to produce reliable results. A new source location method based on coda-wave interferometry (CWI) is relatively insensitive to the number of seismic stations and to the source-to-station azimuthal coverage. Therefore, it opens new avenues for research, for applications in areas with unfavorable recording geometries, and for applications that require a complementary method. This method uses CWI to estimate distances between pairs of seismic events with a similar source mechanism recorded at the same station. These separation estimates are used to solve for the locations of clusters of events relative to one another within a probabilistic framework through optimization. It is even possible to find the relative locations of clusters of events with one single-channel station. Given these advantages, it is likely that one reason that the method is not used more widely is the lack of reliable code that implements this multistage method. Therefore, we have developed a well-commented MATLAB code that does so, and we evaluate examples of its applications. It can be used with seismic data from a single-station channel, and it enables data recorded by different channels and stations to be used simultaneously. It is therefore possible to combine data from permanent yet sparse networks and from temporary arrays closer to the source region. We use the code to apply the location method to a selected data set of the New Ollerton earthquakes in England to demonstrate the validity of the code. The worked example is provided within the package. A way to assess the quality of the location results is also provided.

PCN244 UNBIASED TREATMENT EFFICACY DETECTION METHODS WHEN USING PATIENT REPORTED OUTCOMES IN SURVIVAL-DEPENDENT DROP-OUT

Missing patient reported outcome (PRO) data can arise from several processes. In the context of survival designs, the missingness may very well be outcome-dependent. This type of missing data is known as missing not at random (MNAR). In this circumstance, the PRO scores will produce biased treatment efficacy estimates. This bias can lead to the incorrect inference of no difference in treatment efficacy across arms. Outcome-dependent missingness poses serious challenges and requires the use of statistical approaches that properly adjust for this type of missing data. Currently, the state of the art in modeling PROs in a survival analysis context is to simultaneously model observed scores and the drop-out mechanism. This is referred to as a joint process model. However, interest in item response theory (IRT) approaches has grown in recent years. An IRT-based framework has been developed for evaluating PRO-based scores in survival analysis context. A longitudinal IRT model that incorporates the drop-out mechanism has been created. This allows the IRT scores and the drop-out mechanism to be modeled simultaneously. A simulation study based on empirical PRO data is employed here to illustrate the advantages of such an approach. The results of the simulation study show that the IRT approach to jointly model Quality of Life (QoL) scores and drop-out allows for the accurate estimation of separation of the treatment arms. Other approaches, such as a longitudinal IRT model that ignores drop-out, fail to detect separation. An IRT-based method has been developed that can better estimate the treatment effect in a survival analysis context. Notably, this approach can be implemented in standard commercial IRT software.

Quantum-limited estimation of the axial separation of two incoherent point sources

Improving axial resolution is crucial for three-dimensional optical imaging systems. Here we present a scheme of axial superresolution for two incoherent point sources based on spatial mode demultiplexing. A radial mode sorter is used to losslessly decompose the optical fields into a radial mode basis set to extract the phase information associated with the axial positions of the point sources. We show theoretically and experimentally that, in the limit of a zero axial separation, our scheme allows for reaching the quantum Cram\'er-Rao lower bound and thus can be considered as one of the optimal measurement methods. Unlike other superresolution schemes, this scheme does not require neither activation of fluorophores nor sophisticated stabilization control. Moreover, it is applicable to the localization of a single point source in the axial direction. Our demonstration can be useful to a variety of applications such as far-field fluorescence microscopy.

The collective reserving model

This paper sets out a model for analysing claims development data, which we call the collective reserving model (CRM). The model is defined on the individual claim level and it produces separate IBNR and RBNS reserve estimators at the collective level without using any approximations. The CRM is based on ideas from a paper by Verrall, Nielsen and Jessen (VNJ) from 2010 in which a model is proposed that relies on a claim giving rise to a single payment. This is generalised by the CRM to the case of multiple payments per claim. All predictors of outstanding claims payments for the VNJ model are shown to hold for this new model. Moreover, the quasi-Poisson GLM estimation framework will be applicable as well, but without using an approximation. Furthermore, analytical expressions for the variance of the total outstanding claims payments are given, with a subdivision on IBNR and RBNS claims. To quantify the effect of allowing only one payment per claim, the model is related and compared to the VNJ model, in particular by looking at variance inequalities. The double chain ladder (DCL) method is discussed as an estimation method for this new model and it is shown that both the GLM- and DCL-based estimators are consistent in terms of an exposure measure. Lastly, both of these methods are shown to asymptotically reproduce the regular chain ladder reserve estimator when restricting predictions to the lower right triangle without the tail, motivating the chain ladder technique as a large-exposure approximation of this model.

Modified regression estimators using robust regression methods and covariance matrices in stratified random sampling

This article proposes new regression-type estimators by considering Tukey-M, Hampel M, Huber MM, LTS, LMS and LAD robust methods and MCD and MVE robust covariance matrices in stratified sampling. Theoretically, we obtain the mean square error (MSE) for these estimators. We compare the efficiencies based on MSE equations, between the proposed estimators and the traditional combined and separate regression estimators. As a result of these comparisons, we observed that our proposed estimators give more efficient results than traditional approaches. And, these theoretical results are supported with the aid of numerical examples and simulation based on data sets that include outliers.

Assumptions of the process-dissociation procedure are violated in implicit sequence learning.

In implicit sequence learning, a process-dissociation (PD) approach has been proposed to dissociate implicit and explicit learning processes. Applied to the popular generation task, participants perform two different task versions: inclusion instructions require generating the transitions that form the learned sequence; exclusion instructions require generating transitions other than those of the learned sequence. Whereas accurate performance under inclusion may be based on either implicit or explicit knowledge, avoiding to generate learned transitions requires controllable explicit sequence knowledge. The PD approach yields separate estimates of explicit and implicit knowledge that are derived from the same task; it therefore avoids many problems of previous measurement approaches. However, the PD approach rests on the critical assumption that the implicit and explicit processes are invariant across inclusion and exclusion conditions. We tested whether the invariance assumptions hold for the PD generation task. Across three studies using first-order as well as second-order regularities, invariance of the controlled process was found to be violated. In particular, despite extensive amounts of practice, explicit knowledge was not exhaustively expressed in the exclusion condition. We discuss the implications of these findings for the use of process-dissociation in assessing implicit knowledge. (PsycINFO Database Record (c) 2019 APA, all rights reserved).