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PubMed1992

[Jackknife and bootstrap].

The jackknife and the bootstrap are two non parametric methods which provide estimates- of the bias and the variance of an estimator, without any assumption about its statistical distribution. The jackknife is based on the observation of the estimator for subsamples, generally of size n-1, obtained from the original sample. The bootstrap is based on the observation of the estimator on size n samples drawn from the original sample. The two methods are presented, their principle is illustrated through their application to simple examples and to more complex epidemiological problems.

Bias↗

PubMed1987

Bootstrapped potential circadian harbingers if not determinants of cardiovascular risk.

Among 12 endocrine variables in blood from clinically healthy adult women sampled systematically around the clock and the year, discriminant analysis methods have singled out certain hormones in certain seasons as classifiers for a high or low risk of developing diseases associated with a high circadian rhythm-adjusted mean (midline estimating statistic of rhythm, MESOR, M) of blood pressure, i.e., risk of M-hypertension (RMH). Before extending the labor intensive, costly data base, showing circadian changes with RMH, we reanalyzed available data by circadian bootstrapping, complementing earlier circannual bootstrapping. Differences in circadian M for aldosterone in all four seasons and for TSH in spring and summer (the only seasons checked), but not for the cortisol M checked in spring and summer, are validated, as are differences in circadian amplitude for TSH in spring and summer and aldosterone in spring. Identification of classifiers provides cost-effective, time-specified endocrine checks complementing the targeted automatic monitoring of blood pressure as part of a system of chronobioengineering for health maintenance.

Adolescent↗

PubMed1995

Correction for covariate measurement error in generalized linear models--a bootstrap approach.

A two-phase bootstrap method is proposed for correcting covariate measurement error. Two data sets are needed: validation data for approximating the measurement model and data with a response variable. Bootstrap samples from both the data sets validation data are taken. Parameter estimates of the generalized linear model are calculated using expectations of the measurement model from the validation data as explanatory variables. The method is compared through simulation in logistic regression with the correction method proposed by Rosner, Willet, and Spiegelman (1991, Statistics in Medicine 8, 1051-1069). A real data example is also presented.

Age Factors↗

PubMed2026

Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

BACKGROUND: Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression levels, helping to uncover how genetic variation contributes to gene regulation. While traditional eQTL analyses rely on bulk RNA-seq data, recent advances in single-cell RNA sequencing (scRNA-seq) have made it possible to detect cell-type-specific eQTLs. However, the inherent sparsity and heterogeneity of scRNA-seq data present major challenges for standard modeling approaches. METHODS: In this paper, we propose a novel statistical framework, Bootstrap Penalized Hurdle regression model (BPHurdle), designed specifically for scRNA-seq data. BPHurdle employs a hurdle modeling framework, where a logistic component accounts for the excess zeros in single-cell expression data, and a Poisson component jointly evaluates the effects of multiple SNPs on positive gene expression levels. RESULTS: Through simulation studies, we show that BPHurdle achieves high accuracy and robustness in identifying regulatory variants. We further demonstrate its utility on a real dataset through a case study focusing on a subset of differentially expressed genes, where it successfully identifies reliable cell-type-specific eQTLs. CONCLUSIONS: Overall, BPHurdle offers an advanced and flexible approach for single-cell eQTL mapping, providing deeper insight into the genetic regulation of gene expression at cellular resolution.

Quantitative Trait Loci↗

PubMed1990

Bootstrapping: a tool for clinical research.

The use of the bootstrap sampling technique is applied to the type of data found in clinical research. Confidence intervals are computed for simulated values by use of SAS. By applying this approach, clinical researchers are free to explore topics that do not meet the requirements of traditional statistical analytic methods.

Acquired Immunodeficiency Syndrome↗

PubMedDate not supplied

The use of GLIM and the bootstrap in assessing a clinical trial of two drugs.

An approach is described for estimating the dose of a new drug which is equipotent to an established dose of an old drug. The approach is basically that of the parallel-line assay but it can allow for concomitant variables and, by exploiting the facilities available in the statistical computer package GLIM (generalized linear interactive modelling), the approach can be applied when the residuals conform to one of a number of distributions and, with suitable safeguards, to continuous, discrete and even 'scored' responses. In some circumstances, it is necessary to obtain confidence limits by Efron's 'bootstrap' technique. The method is illustrated with results from a trial of two premedicant drugs in children.

Biometry↗

PubMedDate not supplied

Constructing a bootstrap confidence interval for the unknown concentration in radioimmunoassay.

The statistical problem associated with radioimmunoassay is known as calibration or inverse regression. In the current study, we propose a bootstrap procedure aimed at constructing an inverse confidence interval for the univariate calibration problem. The calibration curve is estimated either parametrically or by non-parametric regression. The methods are illustrated by an example.

Calibration↗

PubMed1992

What sort of innate structure is needed to "bootstrap" into syntax?

The paper starts from Pinker's theory of the acquisition of phrase structure; it shows that it is possible to drop all the assumptions about innate syntactic structure from this theory. These assumptions can be replaced by assumptions about the basic structure of semantic representation available at the outset of language acquisition, without penalizing the acquisition of basic phrase structure rules. Essentially, the role played by X-bar theory in Pinker's model would be played by the (presumably innate) structure of the language of thought in the revised parallel model. Bootstrapping and semantic assimilation theories are shown to be formally very similar, though making different primitive assumptions. In their primitives, semantic assimilation theories have the advantage that they can offer an account of the origin of syntactic categories instead of postulating them as primitive. Ways of improving on the semantic assimilation version of Pinker's theory are considered, including a way of deriving the NP-VP constituent division that appears to have a better fit than Pinker's to evidence on language variation.

Child, Preschool↗

PubMed1985

Comparison of receiver operating curves derived from the same population: a bootstrapping approach.

The receiver operating curve (ROC) gives a representation of sensitivity and specificity of a prediction model when varying the cutpoint of a decision rule on a whole spectrum. Evaluation of two models established (or tested) in the same population of patients warrants a valid statistical comparison of their ROC curves. Hanley et al. recently provided a method for overall comparison of ROC curves (J. A. Hanley and B. J. McNeil, Radiology 148, 839-843, 1983). Often ROC curves cross, or differ in only a part of their courses. Bootstrapping of ROC curves is proposed as a graphical check for the statistical significance of differences confined to a part of the curve. An example comparing two models of prediction of coronary artery disease progression is given to illustrate this new approach.

Coronary Angiography↗

PubMed1994

Application of a statistical bootstrapping technique to calculate growth rate variance for modelling psychrotrophic pathogen growth.

The inherent variability or 'variance' of growth rate measurements is critical to the development of accurate predictive models in food microbiology. A large number of measurements are typically needed to estimate variance. To make these measurements requires a significant investment of time and effort. If a single growth rate determination is based on a series of independent measurements, then a statistical bootstrapping technique can be used to simulate multiple growth rate measurements from a single set of experiments. Growth rate variances were calculated for three large datasets (Listeria monocytogenes, Listeria innocua, and Yersinia enterocolitica) from our laboratory using this technique. This analysis revealed that the population of growth rate measurements at any given condition are not normally distributed, but instead follow a distribution that is between normal and Poisson. The relationship between growth rate and temperature was modeled by response surface models using generalized linear regression. It was found that the assumed distribution (i.e. normal, Poisson, gamma or inverse normal) of the growth rates influenced the prediction of each of the models used. This research demonstrates the importance of variance and assumptions about the statistical distribution of growth rates on the results of predictive microbiological models.

Bacteria↗

PubMed1989

Using permutation tests and bootstrap confidence limits to analyze repeated events data from clinical trials.

In clinical trials comparing treatments for superficial bladder cancer, patients are at risk of repeated recurrences of their disease. Statistical methods of analyzing such data are required. This article presents a nonparametric approach. A statistical test to compare the recurrence or tumor rates in two treatment groups, using the randomization distribution, is described. Confidence intervals for the rate ratio are determined from the bootstrap distribution. The implementation of both requires Monte Carlo methods. Computer simulations support the use of these nonparametric methods when there are more than 60 recurrences in each treatment group. An example illustrating their use is given. The strategy adopted for analysis of these data could be applied to other clinical trials where standard methodology is inappropriate.

Biometry↗

PubMed1992

Estimating effective population size from samples of sequences: a bootstrap Monte Carlo integration method.

We would like to use maximum likelihood to estimate parameters such as the effective population size N(e) or, if we do not know mutation rates, the product 4N(e) mu of mutation rate per site and effective population size. To compute the likelihood for a sample of unrecombined nucleotide sequences taken from a random-mating population it is necessary to sum over all genealogies that could have led to the sequences, computing for each one the probability that it would have yielded the sequences, and weighting each one by its prior probability. The genealogies vary in tree topology and in branch lengths. Although the likelihood and the prior are straightforward to compute, the summation over all genealogies seems at first sight hopelessly difficult. This paper reports that it is possible to carry out a Monte Carlo integration to evaluate the likelihoods approximately. The method uses bootstrap sampling of sites to create data sets for each of which a maximum likelihood tree is estimated. The resulting trees are assumed to be sampled from a distribution whose height is proportional to the likelihood surface for the full data. That it will be so is dependent on a theorem which is not proven, but seems likely to be true if the sequences are not short. One can use the resulting estimated likelihood curve to make a maximum likelihood estimate of the parameter of interest, N(e) or of 4N(e) mu. The method requires at least 100 times the computational effort required for estimation of a phylogeny by maximum likelihood, but is practical on today's work stations. The method does not at present have any way of dealing with recombination.

Base Sequence↗

PubMed1990

Input evidence regarding the semantic bootstrapping hypothesis.

The input language addressed to 18 language-learning children (MLU 1.00-3.00) was analysed so as to assess the quality of the semantic-syntactic correspondence posited by the semantic bootstrapping hypothesis. The correspondence appears to be quite satisfactory with little variation from the lower to the higher MLUs. All the persons and things referred to in the corpora were labelled by the mothers using nouns. All the actions referred to were labelled using verbs. Most of the attributive information was conveyed by adjectives. Spatial information was expressed through the use of spatial prepositions. As to the functional categories, all agents of actions and causes of events were encoded as subjects of sentences. All patients, themes, sources, goals, locations, and instruments were encoded as objects of sentences (either direct or oblique). This good semantic-syntactic correspondence may make the child's construction of grammatical categories easier.

Child Language↗

PubMedDate not supplied

Bootstrap sensitometry for nuclear medicine.

A full radioscintigraphic monitor/film system sensitometric curve has been obtained utilizing a bootstrap technique in which individual characteristic curve segments, obtained from stepwedge-graded exposures, are tied together at the point of overlap. Curve segments were first smoothed by employing the linearized form of the logistic distribution function. This function allows calculation of gradient-exposure and gradient-density relations for the full characteristic curve.

Humans↗

PubMed1989

Testing separate families of segregation hypotheses: bootstrap methods.

Aspects of the statistical modeling and assessment of hypotheses concerning quantitative traits in genetics research are discussed. It is suggested that a traditional approach to such modeling and hypothesis testing, whereby competing models are "nested" in an effort to simplify their probabilistic assessment, can be complimented by an alternative statistical paradigm - the separate-families-of-hypotheses approach to segregation analysis. Two bootstrap-based methods are described that allow testing of any two, possibly non-nested, parametric genetic hypotheses. These procedures utilize a strategy in which the unknown distribution of a likelihood ratio-based test statistic is simulated, thereby allowing the estimation of critical values for the test statistic. Though the focus of this paper concerns quantitative traits, the strategies described can be applied to qualitative traits as well. The conceptual advantages and computational ease of these strategies are discussed, and their significance levels and power are examined through Monte Carlo experimentation. It is concluded that the separate-families-of-hypotheses approach, when carried out with the methods described in this paper, not only possesses some favorable statistical properties but also is well suited for genetic segregation analysis.

Alleles↗

PubMedDate not supplied

Circannual bootstrapping complements pattern discrimination in the assessment of endocrine markers for an expansive personality (EP).

The bootstrap distribution of the difference in the circannual mesor of DHEA-S, TSH and LH between healthy adult women of a lowly or highly expansive personality, assessed by scale 9 of an abbreviated Minnesota Multiphasic Personality Inventory, validates the potential classifying role of these hormones, originally singled out by methods of pattern discrimination.

Adult↗

PubMedDate not supplied

Bootstrapping and added data discriminate, at low blood pressures, neuroendocrine risk of developing mesor-hypertension.

Under room-restricted conditions in a clinical research center, blood pressure and circulating aldosterone and TSH, sampled along 24-h and seasonal scales, reveal differences between small groups of young adult clinically healthy women at high or low risk of developing a high blood pressure. In view of the small sample sizes, data on additional age groups were added and both the original and the extended samples were further analyzed by bootstrapping. Monte Carlo procedures thus applied support the validity of the rhythm-stage-dependent endocrine and blood pressure differences as a function of the risk of developing a high blood pressure.

Aldosterone↗

PubMed1983

A bootstrap model for the proximodistal pattern formation in vertebrate limbs.

For the sequential determination of proximodistal structures during the outgrowth of vertebrate limbs, a 'bootstrap'-mechanism is proposed: by increasing feedback of more distally determined cells onto the production of a morphogen at the apical ectodermal ridge a successive increase of the morphogen concentration is achieved during outgrowth. The model accounts for the formation of a progress-zone at the limb tip, for the correct regeneration after truncation, for the presence and absence of proximodistal intercalation after certain graft experiments in amphibian limbs, for the tendency with which distal structures form in proximal position after certain experimental manipulations and for the intimate coupling of the anteroposterior and the proximodistal axes.

Amphibians↗