Jack Knife Research Articles

Effectiveness Of Jackknife Stretching On Hamstring Tightness With Low Back Pain Among Information Technology Professionals - Experimental Study

Background: Hamstring muscles are located at the back of the thigh and the primary action of hamstring muscles is flexion of the knee. It is an effective self-stretching technique; it combines static and dynamic stretching and can be performed without any equipment. Methods: For Jack-knife stretching, participants started in a full squat position, gripping both ankles. The subjects were instructed to extend their knees as much as possible while bringing their chest close to their thighs, holding this position for 10 seconds before returning to the starting position6 . This sequence was repeated five times with a 10-second rest period between five repetitions, performed twice a week for two weeks. Results: Results shows that Jack Knife stretching is very effective in reducing Hamstring Tightness and reduction in low back pain. There is a significant difference in pre and post-intervention scores of NPRS with P < 0.001 and QUEBEC scores with improved functional ability having P < 0.001 Conclusion: The study concludes that Jack Knife stretching is effective in reducing Hamstring Tightness and reduction in low back pain. It was also concluded that there is increased functional ability of the Hamstring muscle

Comparison of Propensity Score Weighting Methods to Remove Selection Bias in Average Treatment Effect Estimates

In this Monte Carlo simulation study, the performance of six different propensity score methods implemented through weighting cases was investigated: inverse probability of treatment weighting, truncated inverse probability of treatment weighting, propensity score stratification, marginal mean weighting through propensity score stratification, optimal full propensity score matching, and marginal mean weighting through optimal full propensity score matching. These methods aim to reduce selection bias in estimates of the average treatment effect (ATE) in observational studies. For the estimation of standard errors of the ATE with weights, three methods were compared: weighted least squares (WLS), Taylor series linearization (TSL), and jackknife (JK). Results indicated that covariance adjustment extensions of the investigated propensity score methods, in combination with TSL and JK standard error estimation methods, remove the selection bias appropriately and provide the most accurate standard errors under the simulated conditions.

EFFECT OF CORE STRENGTH TRAINING PROGRAM ON ANAEROBIC POWER, SPEED AND STATIC BALANCE IN VOLLEYBALL PLAYERS

The aim of this study is to examine the effect of the core strength training program applied to volleyball players on anaerobic power, speed and static balance. A total of 18 male volleyball players between the ages of 18-22, who regularly practice volleyball, participated in our study voluntarily. The subjects were divided into two different groups as the experimental group (n=9, age: 19.41±2.48) and the control group (n=9, age: 21.12±2.32). The control group continued their normal volleyball training. In the experimental group, in addition to volleyball training, a core strength training program was applied 3 days a week for 6 weeks. Core exercises are known as Mountain Climbers, Bicycle Crunch, Leg Raises, Russian Twist, Side Plank, Bridge, Abdominal Crunch, Jack Knife, Superman, Plank Jack were performed on the experimental group. Static balance scores were measured with the Biodex Balance System balance test device. A speed test (30 m) was applied. Anaerobic power was measured with the vertical jump test. Independent Samples T-Test was used for statistical evaluation between the experimental and control groups. Paired Samples T-Test was used for in-group comparisons. After the core training program was applied to the experimental group, significance was determined in the speed, anaerobic power and static balance scores (p<0.05). There was a significant difference between the groups in speed, anaerobic power and static balance scores in favor of the experimental group (p<0.05). As a result, it can be said that the core strength training program applied to volleyball players has a positive effect on speed, anaerobic power and static balance scores.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0959/a.php" alt="Hit counter" /></p>

DCA Method in Mineral Exploration, Example: Predict the Location of New Samples

As the name implies, Discriminant Correspondence Analysis (DCA) is a format of Discriminant analysis (DA) and correspondence analysis (CA). Like differential analysis, the goal is to categorize observations into predefined groups and, like Correspondence analysis, to use nominal variables. The main idea of the DCA is to represent each set of observations and perform a simple Correspondence analysis on the variables (a matrix). The main observations are complementary elements, and each observation is attributed to the closest group. A comparison between the predictions and predictions of classification leads to evaluating the Discriminant correspondence. This information can be used for a similar case to classify new observations, and the validation of estimates can also be examined using cross-validation techniques such as Jack Knife or Bootstrap. For example, samples were taken from different regions. After scoring the parameters and training in this analysis, a new sample was entered, and a group (region) was determined compared to the previous samples.

Efficiency Comparison of New Adjusted Nonparametric and Parametric Statistics Interval Estimation Methods in the Simple Linear Regression Model

In this research, the authors were interested in an efficiency comparison study of new adjusted nonparametric and parametric statistics interval estimation methods in the simple linear regression model. The independent variable and the error came from normal, scale-contaminated normal, and gamma distributions. Six point estimations were performed, for example, least squares, Bayesian, Jack knife, Theil, optimum-type Theil, and new adjusted Theil–Sen and Siegel methods in the simple linear regression model with 1,000 iterations. The criteria used to consider in this study were the coefficient of the confidence interval and the average width of the confidence interval used to compare and determine the optimal effectiveness for six interval estimations of the simple linear regression model. In the interval estimation for normal and scale-contaminated normal distributions ofβ0, the least squares method had the narrowest average width of confidence interval. For the interval estimation ofβ1, the Bayesian method had the narrowest average width of confidence interval in a small variance of 1, followed by the same of optimum-type Theil and new adjusted Theil–Sen and Siegel methods, and Theil method, respectively. In the interval estimation for gamma distribution ofβ1, the Bayesian method had the narrowest average width of confidence interval, followed by optimum-type Theil, new adjusted Theil–Sen and Siegel, and Theil methods, respectively. The optimum-type Theil method was good for medium sample size, while Theil and new adjusted Theil–Sen and Siegel methods were good for small and large sample sizes. Therefore, new adjusted Theil–Sen and Siegel method can be used in many situations and can be used in place of optimum-type Theil and Theil methods for nonparametric statistics interval estimation.

Efficient sonic log estimations by geostatistics, empirical petrophysical relations, and their combination: Two case studies from Iranian hydrocarbon reservoirs

In this study, the potential of geostatistical (GS) methods in estimating petrophysical logs (Vp sonic logs here), independently and in combination with empirical petrophysical relations (PRs) was evaluated. Moreover, the efficiencies of these two methods were compared with each other and with that of a simple PR which was Gardner's Relation (GR) here. The GS-GR method combines GS with GR to acquire a higher accuracy beyond a simple geostatistical prediction which is already expected to have a higher accuracy than that of GR. For this purpose, two different data-sets from Iranian hydrocarbon reservoirs were used. In the first case, three adjacent wells from a reservoir consisting dominantly of carbonate rocks with a rather high hydrocarbon saturation were used. To evaluate the generalizability of the propsed techniques, they were assessed on another data-set from a well in a different hydrocarbon reservoir, mainly consisting of the sandstone-carbonate lithology (with a lower hydrocarbon saturation). To estimate the sonic logs of compressional wave velocities (Vp), Gardner's Relation (GR) was implemented here using the existing density logs in the wells. Then, several univariate and bivariate geostatistical methods (the latter combined with GR) were used to assess the capabilities of the GS and the combined GS-GR methods. To evaluate the efficiencies of the GS and GS-GR methods, Cross-Validation (CV) and Jackknife (JK) techniques were employed here. Based on the CV and JK assessments, for the both reservoirs, the GS and methods especially those combined with GR (i.e. GS-GR), generally provided considerably higher accuracies than those of the GR method. GS-GR framework facilitated the integration of the secondary variable of density (ρ) here to further increase the accuracy of Vp estimations. Hence, geostatistics (GS), especially combined with the petrophysical relations (GS-PR) could be suggested as an accurate and functional tool in estimating the missing logs, having some logs from the same or the other neighboring wells.

Jackknife for nonlinear estimating equations

In mixture with varying concentrations model (MVC) one deals with a nonhomogeneous sample which consists of subjects belonging to a fixed number of different populations (mixture components). The population which a subject belongs to is unknown, but the probabilities to belong to a given component are known and vary from observation to observation. The distribution of subjects’ observed features depends on the component which it belongs to. Generalized estimating equations (GEE) for Euclidean parameters in MVC models are considered. Under suitable assumptions the obtained estimators are asymptotically normal. A jackknife (JK) technique for the estimation of their asymptotic covariance matrices is described. Consistency of JK-estimators is demonstrated. An application to a model of mixture of nonlinear regressions and a real life example are presented.

Perineal stapled prolapse resection in combination with Thiersch operation for relapsed rectal prolapse: a case report

BackgroundTreatment options for complete rectal prolapse include over 100 procedures. In previous reports, operative rectal prolapse repair, regardless of the technique by perineal approach, was associated with high recurrence rates. However, there is no consensus on the optimal surgical procedure for relapsed rectal prolapse.Case presentationA 97-year-old woman was admitted to our hospital with a chief complaint of complete rectal prolapse measuring > 5 cm. The patient had a history of laparoscopic anterior suture rectopexy without sigmoid resection under general anesthesia for complete rectal prolapse one year prior. The patient’s postoperative course was uneventful. However, her dementia worsened (Hasegawa’s dementia scale: 5/30 points) after the first operation. Further, moderate-to-severe aortic valve stenosis was first diagnosed with heart failure 6 months after the operation. Nine months after the initial surgery, she experienced a recurrence of complete rectal prolapse measuring approximately 5 cm. Considering the coexistence of advanced age, severe dementia, and aortic valve stenosis, surgery under general anesthesia was not indicated. Perineal stapled prolapse resection in combination with the t operation was planned because of its minimal invasiveness and shortened hospital stay. The procedure was performed by a team of two surgeons in the jack knife position, under spinal anesthesia.The prolapse was cut along the long-axis direction with three linear staplers and resected along the short-axis direction with four linear staplers. The cross-section of the linear stapler was reinforced with 3-0 Vicryl sutures. After rectal resection, the Thiersch operation using 1-0 nylon thread 1 cm away from the anal verge was additionally performed. The operative time was 24 min, and intraoperative blood loss was 1 mL. The postoperative course was uneventful. Three months after the operation, no recurrence was observed, and defecation function was good with improvements of Wexner score.ConclusionsPerineal stapled prolapse resection in combination with the Thiersch operation could be a useful option for patients with relapsed rectal prolapse and with poor general condition, who are not indicated for other surgical procedures.

Effect of Jack Knife Stretching Versus Proprioceptive Neuromuscular Facilitation (Hold Relax) Stretching Technique in Asymptomatic Individuals with Hamstring Tightness: A Randomized Clinical Trial

Background: Study is to compare the effectiveness of Jack knife stretching and Proprioceptive Neuromuscular Facilitation (PNF) stretching Technique in asymptomatic individuals with hamstring tightness. Aims and Objectives : 1. To study the effect of jack-knife stretching on the individuals with asymptomatic hamstring tightness in terms of Active knee extension test, finger to floor, passive SLR and LEFS Questionnaire.2.To Study the effect of PNF stretching on individuals with asymptomatic hamstring tightness. 3.To compare the effect of Jack knife and PNF stretching in individuals with hamstring tightness. Material and Method: Ethical approval was obtained. 56 healthy subjects were assigned into two groups. PNF (hold relax) stretching technique was given to the Group A and Group B received Jack Knife stretching. Active knee extension test, Finger to floor test, Passive single leg raise test and Lower extremity functional scale these parameters were measured . Results: Intra-group comparison for all the outcome measures in both the groups showed statistical significance. Inter-group comparison for all the outcome measures for both the groups had differences but showed statistical significance. Group B Jack knife stretching was more effective than group A PNF (hold relax) stretching. Conclusion: The present study results indicate that to increase hamstring flexibility both the treatment techniques i.e PNF stretching and jack knife stretching are effective in asymptomatic individuals with hamstring tightness. Thus, our study shows jack knife stretching gives immediate effect due to closed pack position of stretching compared to open pack position of PNF group

Comparison of Parameter Estimator Efficiency Levels of Path Analysis with Bootstrap and Jack Knife (Delete-5) Resampling Methods on Simulation Data

In practice, the assumptions of normality are often not met, this causes the estimation of the resulting parameters to be less efficient. Problems with the assumption that normality is not met can be overcome by resampling. The use of resampling allows data to be applied free from distribution assumptions. In this study, a simulation study was carried out by applying bootstrap resampling and jackknife resampling (delete-5) on path analysis assuming that the normality of the alignment was not met and the resampling amount set at 1000 with the degree of closeness between variables consisting of low closeness, medium closeness, high closeness and closeness level representing the level low to high closeness. Based on the simulation results, the resampling 1000 magnitude is able to overcome the problem of the assumptions of unmet normality. In addition, a comparison between bootstrap and jackknife resampling for conditions of side normality assumptions is not fulfilled and the closeness of the relationship between low, medium, high and closeness variables representing low to high closeness levels, the estimation results of path analysis parameters obtained by resampling jackknife are more efficient than resampling bootstrap.

A New Proposed Estimator for Reducing Bias Due to Undetected Species

The present paper addresses a new approach to reduce bias when there are undetected species in a plot. Partially density matrix plays essential role in this new proposed estimator. The performance of the new proposed estimator (Ĥ0) was compared to bias-corrected MLE (MLEBC), Jackknife (JK) and the proposed estimator of Chao and Shen (Ĥcs) using Principle component analysis (PCA). The result of the first PCA applied to the data including the estimators’ values of the assemblages showed that Ĥ0 is located between JK and Ĥcs and its’ nearest neighbor becomes JK. The second PCA was applied to the data belonging to the relative estimator values between the pairwise assemblages and, it was found that Ĥ0 is still located between JK and Ĥcs but its’ nearest neighbor becomes Ĥcs in this time along the first axis. Those results were evaluated that Ĥ0 is a better estimator than MLEBC. Thus the new proposed estimator (Ĥ0) can also be used as an alternative bias-corrected estimator in addition to the other estimators.

Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap

Mixture models with (multivariate) Gaussian components are a popular tool in model-based clustering. Such models are often fitted by a procedure that maximizes the likelihood, such as the EM algorithm. At convergence, the maximum likelihood parameter estimates are typically reported, but in most cases little emphasis is placed on the variability associated with these estimates. In part this may be due to the fact that standard errors are not directly calculated in the model-fitting algorithm, either because they are not required to fit the model, or because they are difficult to compute. The examination of standard errors in model-based clustering is therefore typically neglected. Sampling based methods, such as the jackknife (JK), bootstrap (BS) and parametric bootstrap (PB), are intuitive, generalizable approaches to assessing parameter uncertainty in model-based clustering using a Gaussian mixture model. This paper provides a review and empirical comparison of the jackknife, bootstrap and parametric bootstrap methods for producing standard errors and confidence intervals for mixture parameters. The performance of such sampling methods in the presence of small and/or overlapping clusters requires consideration however; here the weighted likelihood bootstrap (WLBS) approach is demonstrated to be effective in addressing this concern in a model-based clustering framework. The JK, BS, PB and WLBS methods are illustrated and contrasted through simulation studies and through the traditional Old Faithful data set and also the Thyroid data set. The MclustBootstrap function, available in the most recent release of the popular R package mclust, facilitates the implementation of the JK, BS, PB and WLBS approaches to estimating parameter uncertainty in the context of model-based clustering. The JK, WLBS and PB approaches to variance estimation are shown to be robust and provide good coverage across a range of real and simulated data sets when performing model-based clustering; but care is advised when using the BS in such settings. In the case of poor model fit (for example for data with small and/or overlapping clusters), JK and BS are found to suffer from not being able to fit the specified model in many of the sub-samples formed. The PB also suffers when model fit is poor since it is reliant on data sets simulated from the model upon which to base the variance estimation calculations. However the WLBS will generally provide a robust solution, driven by the fact that all observations are represented with some weight in each of the sub-samples formed under this approach.

The Role of Objectively Measured, Altered Physical Activity Patterns for Body Mass Index Change during Inpatient Treatment in Female Patients with Anorexia Nervosa.

Increased physical activity (PA) affects outcomes in patients with anorexia nervosa (AN). To objectively assess PA patterns of hospitalized AN patients in comparison with healthy, outpatient controls (HC), and to analyze the effect of PA on Body Mass Index (BMI) change in patients with AN, we measured PA in 50 female patients with AN (median age = 25 years, range = 18–52 years; mean BMI = 14.4 ± 2.0 kg/m2) at the initiation of inpatient treatment and in 30 female healthy controls (median age = 26 years, range = 19–53 years; mean BMI = 21.3 ± 1.7 kg/m2) using the SenseWear™ armband. Duration of inpatient stay and weight at discharge were abstracted from medical records. Compared with controls, AN patients spent more time in very light-intensity physical activity (VLPA) (median VLPA = 647 vs. 566 min/day, p = 0.004) and light-intensity physical activity (LPA) (median LPA = 126 vs. 84 min/day, p < 0.001) and less time in moderate-intensity physical activity (MPA) (median MPA = 82 vs. 114 min/day, p = 0.022) and vigorous physical activity (VPA) (median VPA = 0 vs. 16 min/day, p < 0.001). PA and BMI increase were not associated in a linear model, and BMI increase was mostly explained by lower admission BMI and longer inpatient stay. In a non-linear model, an influence of PA on BMI increase seemed probable (jack knife validation, r2 = 0.203; p < 0.001). No direct association was observed between physical inactivity and BMI increase in AN. An altered PA pattern exists in AN patients compared to controls, yet the origin and consequences thereof deserve further investigation.

IDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features

DNA-binding proteins play a very important role in the structural composition of the DNA. In addition, they regulate and effect various cellular processes like transcription, DNA replication, DNA recombination, repair and modification. The experimental methods used to identify DNA-binding proteins are expensive and time consuming and thus attracted researchers from computational field to address the problem. In this paper, we present iDNAProt-ES, a DNA-binding protein prediction method that utilizes both sequence based evolutionary and structure based features of proteins to identify their DNA-binding functionality. We used recursive feature elimination to extract an optimal set of features and train them using Support Vector Machine (SVM) with linear kernel to select the final model. Our proposed method significantly outperforms the existing state-of-the-art predictors on standard benchmark dataset. The accuracy of the predictor is 90.18% using jack knife test and 88.87% using 10-fold cross validation on the benchmark dataset. The accuracy of the predictor on the independent dataset is 80.64% which is also significantly better than the state-of-the-art methods. iDNAProt-ES is a novel prediction method that uses evolutionary and structural based features. We believe the superior performance of iDNAProt-ES will motivate the researchers to use this method to identify DNA-binding proteins. iDNAProt-ES is publicly available as a web server at: http://brl.uiu.ac.bd/iDNAProt-ES/.

Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm

Stroke risk stratification based on grayscale morphology of the ultrasound carotid wall has recently been shown to have a promise in classification of high risk versus low risk plaque or symptomatic versus asymptomatic plaques. In previous studies, this stratification has been mainly based on analysis of the far wall of the carotid artery. Due to the multifocal nature of atherosclerotic disease, the plaque growth is not restricted to the far wall alone. This paper presents a new approach for stroke risk assessment by integrating assessment of both the near and far walls of the carotid artery using grayscale morphology of the plaque. Further, this paper presents a scientific validation system for stroke risk assessment. Both these innovations have never been presented before.The methodology consists of an automated segmentation system of the near wall and far wall regions in grayscale carotid B-mode ultrasound scans. Sixteen grayscale texture features are computed, and fed into the machine learning system. The training system utilizes the lumen diameter to create ground truth labels for the stratification of stroke risk. The cross-validation procedure is adapted in order to obtain the machine learning testing classification accuracy through the use of three sets of partition protocols: (5, 10, and Jack Knife).The mean classification accuracy over all the sets of partition protocols for the automated system in the far and near walls is 95.08% and 93.47%, respectively. The corresponding accuracies for the manual system are 94.06% and 92.02%, respectively. The precision of merit of the automated machine learning system when compared against manual risk assessment system are 98.05% and 97.53% for the far and near walls, respectively. The ROC of the risk assessment system for the far and near walls is close to 1.0 demonstrating high accuracy.

Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm

Classification reliability and accuracy are important components for any computer-aided diagnostic system. This paper presents a dermatology CADx system to automatically classify dermatology images into psoriatic lesion and healthy skin using an online system. The novelty of the system is an exploration of the unique and comprehensive feature space combined with classification in support vector machine (SVM) paradigm. The unique feature space consists of grayscale space, color space and aggressiveness of psoriatic disease such as redness and chaoticness.The proposed CADx framework is conventional in paradigm that it has offline and online components. The offline system trains using unique integrated feature space and apriori dermatologist derived ground truth. This training system yields machine learning parameters. The online system is applied on the incoming test images which get transformed by an online classifier utilizing the offline machine learning parameters. The accuracy of the system is evaluated using cross-validation procedure depending upon one of the three partition protocols such as (5-fold, 10-fold and Jack Knife).The proposed CADx system shows the classification accuracy of 99.53%, 99.66% and 99.81% for 5-fold, 10-fold and Jack Knife protocols respectively for 15 optimal features. Further, our results show that, we can demonstrate the reliability and consistency factor by showing the monotonously rising accuracy with increase in data size. Our system is benchmarked against previous reported systems and outstands besides being unique and novel.

Evaluating Methods to Predict Streamflow at Ungauged Sites Using Regional Flow Duration Curves: A Case Study

Precise information on streamflows is of major importance for planning and monitoring of water resources schemes related to hydro power, water supply, irrigation, flood control, and for maintaining ecosystem. Engineers encounter challenges when streamflow data are either unavailable or inadequate at target locations. To address these challenges, there have been efforts to develop methodologies that facilitate prediction of streamflow at ungauged sites. Conventionally, time intensive and data exhaustive rainfall-runoff models are used to arrive at streamflow at ungauged sites. Most recent studies show improved methods based on regionalization using Flow Duration Curves (FDCs). A FDC is a graphical representation of streamflow variability, which is a plot between streamflow values and their corresponding exceedance probabilities that are determined using a plotting position formula. It provides information on the percentage of time any specified magnitude of streamflow is equaled or exceeded. The present study assesses the effectiveness of two methods to predict streamflow at ungauged sites by application to catchments in Mahanadi river basin, India. The methods considered are (i) Regional flow duration curve method, and (ii) Area Ratio method. The first method involves (a) the development of regression relationships between percentile flows and attributes of catchments in the study area, (b) use of the relationships to construct regional FDC for the ungauged site, and (c) use of a spatial interpolation technique to decode information in FDC to construct streamflow time series for the ungauged site. Area ratio method is conventionally used to transfer streamflow related information from gauged sites to ungauged sites. Attributes that have been considered for the analysis include variables representing hydrology, climatology, topography, land-use/land-cover and soil properties corresponding to catchments in the study area. Effectiveness of the presented methods is assessed using jack knife cross-validation. Conclusions based on the study are presented and discussed.

Jamesbondia, a new subgenus of Alternanthera (Gomphrenoideae, Amaranthaceae) from Central America and the Caribbean Islands

Jamesbondia is formally proposed here as a new subgenus of Alternanthera, a large Neotropical genus, with four species [A. costaricensis Kuntze, A. geniculata Urb., A. olivacea Urb., and A. serpyllifolia (Poir.) Urb.], mostly found in Central America and the Caribbean Islands. In a recent phylogeny using internal transcribed spacer (ITS), trnL-F, and rpl16 data and samples of three of the four species listed above, an unpublished genus Jamesbondia was embedded within Alternanthera. The other three species (not A. costaricensis) clustered in a clade with 97% jackknife (JK) support. Here, to determine the position of A. costaricensis within Alternanthera and taxonomically revise the species in subgenus Jamesbondia, we analyzed ITS, trnL-F, and rpl16 data from GenBank accessions for 31 species and our new molecular data for A. costaricensis to reconstruct a phylogenetic tree confirming that A. costaricensis belongs to the Jamesbondia clade (88–97% JK; 0.88–1.0 posterior probability). Alternanthera costaricensis, Telanthera olivacea Urb., and Achyranthes serpyllifolia Poir. were lectotypified, and we document the distribution of A. costaricensis in Mexico for the first time. Insight from nomenclatural notes, taxonomic descriptions, morphological illustrations, distribution, and a key for species of Jamesbondia provided here will be used to propose a new subgeneric classification of Alternanthera.

101 Occupational exposure to tetrachloroethylene and the risk of bladder cancer: A meta-analysis

ObjectivesThe risk of bladder cancer in persons occupationally exposed to tetrachloroethylene was summarised using a meta-analysis.MethodsStudies were identified from a Pubmed literature search including the terms “drycleaner, dry-cleaning, occupation, tetrachloroethylene,...

An assessment of three variance estimators for the k-nearest neighbour technique

A jackknife (JK), a bootstrap (BOOT), and an empirical difference estimator (EDE) of totals and variance were assessed in simulated sampling from three artificial but realistic complex multivariate populations (N = 8000 elements) organized in clusters of four elements. Intra-cluster correlations of the target variables (Y) varied from 0.03 to 0.26. Time-saving implementations of JK and BOOT are detailed. In simple random sampling (SRS), bias in totals was ≤ 0.4% for the two largest sample sizes (n = 200, 300), but slightly larger for n = 50, and 100. In cluster sampling (CLU) bias was typically 0.1% higher and more variable. The lowest overall bias was in EDE. In both SRS and CLU, JK estimates of standard error were slightly (3%) too high, while the bootstrap estimates in both SRS and CLU were too low (8%). Estimates of error suggested a trend in EDE toward an overestimation with increasing sample size. Calculated 95% confidence intervals achieved a coverage that in most cases was fairly close (± 2%) to the nominal level. For estimation of a population total the EDE estimator appears to be slightly better than the JK estimator.