Home About us Contact
|
Home About us Contact | ||
|
|
||
Nonparametric Estimator (nonparametric + estimator)
Selected AbstractsNonparametric Association Analysis of Exchangeable Clustered Competing Risks DataBIOMETRICS, Issue 2 2009Yu Cheng Summary The work is motivated by the Cache County Study of Aging, a population-based study in Utah, in which sibship associations in dementia onset are of interest. Complications arise because only a fraction of the population ever develops dementia, with the majority dying without dementia. The application of standard dependence analyses for independently right-censored data may not be appropriate with such multivariate competing risks data, where death may violate the independent censoring assumption. Nonparametric estimators of the bivariate cumulative hazard function and the bivariate cumulative incidence function are adapted from the simple nonexchangeable bivariate setup to exchangeable clustered data, as needed with the large sibships in the Cache County Study. Time-dependent association measures are evaluated using these estimators. Large sample inferences are studied rigorously using empirical process techniques. The practical utility of the methodology is demonstrated with realistic samples both via simulations and via an application to the Cache County Study, where dementia onset clustering among siblings varies strongly by age. [source] On the estimation of species richness based on the accumulation of previously unrecorded speciesECOGRAPHY, Issue 1 2002Emmanuelle Cam Estimation of species richness of local communities has become an important topic in community ecology and monitoring. Investigators can seldom enumerate all the species present in the area of interest during sampling sessions. If the location of interest is sampled repeatedly within a short time period, the number of new species recorded is typically largest in the initial sample and decreases as sampling proceeds, but new species may be detected if sampling sessions are added. The question is how to estimate the total number of species. The data collected by sampling the area of interest repeatedly can be used to build species accumulation curves: the cumulative number of species recorded as a function of the number of sampling sessions (which we refer to as "species accumulation data"). A classic approach used to compute total species richness is to fit curves to the data on species accumulation with sampling effort. This approach does not rest on direct estimation of the probability of detecting species during sampling sessions and has no underlying basis regarding the sampling process that gave rise to the data. Here we recommend a probabilistic, nonparametric estimator for species richness for use with species accumulation data. We use estimators of population size that were developed for capture-recapture data, but that can be used to estimate the size of species assemblages using species accumulation data. Models of detection probability account for the underlying sampling process. They permit variation in detection probability among species. We illustrate this approach using data from the North American Breeding Bird Survey (BBS). We describe other situations where species accumulation data are collected under different designs (e.g., over longer periods of time, or over spatial replicates) and that lend themselves to of use capture-recapture models for estimating the size of the community of interest. We discuss the assumptions and interpretations corresponding to each situation. [source] Estimating risk aversion from ascending and sealed-bid auctions: the case of timber auction dataJOURNAL OF APPLIED ECONOMETRICS, Issue 7 2008Jingfeng Lu Estimating bidders' risk aversion in auctions is a challenging problem because of identification issues. This paper takes advantage of bidding data from two auction designs to identify nonparametrically the bidders' utility function within a private value framework. In particular, ascending auction data allow one to recover the latent distribution of private values, while first-price sealed-bid auction data allow one to recover the bidders' utility function. This leads to a nonparametric estimator. An application to the US Forest Service timber auctions is proposed. Estimated utility functions display concavity, which can be partly captured by constant relative risk aversion. Copyright © 2008 John Wiley & Sons, Ltd. [source] Estimation methods for time-dependent AUC models with survival dataTHE CANADIAN JOURNAL OF STATISTICS, Issue 1 2010Hung Hung Abstract The performance of clinical tests for disease screening is often evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Recent developments have extended the traditional setting to the AUC with binary time-varying failure status. Without considering covariates, our first theme is to propose a simple and easily computed nonparametric estimator for the time-dependent AUC. Moreover, we use generalized linear models with time-varying coefficients to characterize the time-dependent AUC as a function of covariate values. The corresponding estimation procedures are proposed to estimate the parameter functions of interest. The derived limiting Gaussian processes and the estimated asymptotic variances enable us to construct the approximated confidence regions for the AUCs. The finite sample properties of our proposed estimators and inference procedures are examined through extensive simulations. An analysis of the AIDS Clinical Trials Group (ACTG) 175 data is further presented to show the applicability of the proposed methods. The Canadian Journal of Statistics 38:8,26; 2010 © 2009 Statistical Society of Canada La performance des tests cliniques pour le dépistage de maladie est souvent évaluée en utilisant l'aire sous la courbe caractéristique de fonctionnements du récepteur (, ROC , ), notée , AUC , . Des développements récents ont généralisé le cadre traditionnel à l'AUC avec un statut de panne binaire variant dans le temps. Sans considérer les covariables, nous commençons par proposer un estimateur non paramétrique pour l'AUC simple et facile à calculer. De plus, nous utilisons des modèles linéaires généralisés avec des coefficients dépendant du temps pour caractériser les AUC, dépendant du temps, comme fonction des covariables. Les procédures d'estimation asociées correspondantes sont proposées afin d'estimer les fonctions paramètres d'intérêt. Les processus gaussiens limites sont obtenus ainsi que les variances asymptotiques estimées afin de construire des régions de confiance approximatives pour les AUC. À l'aide de nombreuses simulations, les propriétés pour de petits échantillons des estimateurs proposés et des procédures d'inférence sont étudiées. Une analyse du groupe d'essais cliniques sur le sida 175 (ACTG 175) est aussi présentée afin de montrer l'applicabilité des méthodes proposées. La revue canadienne de statistique 38: 8,26; 2010 © 2009 Société statistique du Canada [source] Cointegration, Government Spending and Private Consumption: Evidence from JapanTHE JAPANESE ECONOMIC REVIEW, Issue 2 2004Tsung-Wu Ho Assuming a CRRA preference, this paper shows that there is a cointegration restriction implied by the intra-temporal first-order condition in the consumption function. This restriction predicts a cointegrated system of government consumption, private consumption, and their relative price. Our analysis indicates that, first, Johansen's VECM confirms the theoretical prediction that is supported by the data of Japan; moreover, Bierens' (1997) nonparametric estimator severely contradicts with the theoretical model and fits the data poorly; second, Japanese people have increasing willingness to rearrange their consumption over time. Besides, the intratemporal relationship between private and government consumption remains relatively stable over time. [source] A Note on Variance Estimation of the Aalen,Johansen Estimator of the Cumulative Incidence Function in Competing Risks, with a View towards Left-Truncated DataBIOMETRICAL JOURNAL, Issue 1 2010Arthur Allignol Abstract The Aalen,Johansen estimator is the standard nonparametric estimator of the cumulative incidence function in competing risks. Estimating its variance in small samples has attracted some interest recently, together with a critique of the usual martingale-based estimators. We show that the preferred estimator equals a Greenwood-type estimator that has been derived as a recursion formula using counting processes and martingales in a more general multistate framework. We also extend previous simulation studies on estimating the variance of the Aalen,Johansen estimator in small samples to left-truncated observation schemes, which may conveniently be handled within the counting processes framework. This investigation is motivated by a real data example on spontaneous abortion in pregnancies exposed to coumarin derivatives, where both competing risks and left-truncation have recently been shown to be crucial methodological issues (Meister and Schaefer (2008), Reproductive Toxicology26, 31,35). Multistate-type software and data are available online to perform the analyses. The Greenwood-type estimator is recommended for use in practice. [source] Semiparametric Models for Cumulative Incidence FunctionsBIOMETRICS, Issue 1 2004John Bryant Summary. In analyses of time-to-failure data with competing risks, cumulative incidence functions may be used to estimate the time-dependent cumulative probability of failure due to specific causes. These functions are commonly estimated using nonparametric methods, but in cases where events due to the cause of primary interest are infrequent relative to other modes of failure, nonparametric methods may result in rather imprecise estimates for the corresponding subdistribution. In such cases, it may be possible to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimators. The cumulative incidence estimators so obtained are simple to compute and are considerably more efficient than the usual nonparametric estimator, particularly with regard to interpolation of cumulative incidence at early or intermediate time points within the range of data used to fit the function. More surprisingly, they are often nearly as efficient as fully parametric estimators. We illustrate the utility of this approach in the analysis of patients treated for early stage breast cancer. [source] Nonparametric and Parametric Estimation for a Linear Germination-Growth ModelBIOMETRICS, Issue 3 2000S. N. Chiu Summary. Seeds are planted on the interval [0, L] at various locations. Each seed has a location x and a potential germination time t, [0, ,), and it is assumed that the collection of such (x, t) pairs forms a Poisson process in [0, L] × [0, ,) with intensity measure dxd,(t). From each seed that germinates, an inhibiting region grows bidirectionally at rate 2v. These regions inhibit germination of any seed in the region with a later potential germination time. Thus, seeds only germinate in the uninhibited part of [0, L]. We want to estimate , on the basis of one or more realizations of the process, the data being the locations and germination times of the germinated seeds. We derive the maximum likelihood estimator of v and a nonparametric estimator of , and describe methods of obtaining parametric estimates from it, illustrating these with reference to gamma densities. Simulation results are described and the methods applied to some neurobiological data. An Appendix outlines the S-PLUS code used. [source] Evaluating Decision Rules for Nitrogen FertilizationBIOMETRICS, Issue 2 2000T. Antoniadou Summary. It is important, both for farmer profit and for the environment, to correctly dose nitrogen fertilizer for crop growth. Fertilizer recommendations are embodied in decision rules, which give a recommended dose of nitrogen (N) as a function of information available at the time the decision is made. In this paper, we first propose a criterion for evaluating decision rules. The proposed criterion is the expectation of the objective function when the decision rule is implemented. The major problem here is the estimation of this criterion. Two estimators are considered, a model-based and a nonparametric estimator. A simulation study shows that, in essentially all cases, the nonparametric estimator is better or no worse than the model-based estimator. The bias in the nonparametric estimator is always very small. [source] Retrospective analysis of the occurrence of internal malignancy in patients treated with PUVA between 1986 and 1999 in South WarwickshireCLINICAL & EXPERIMENTAL DERMATOLOGY, Issue 2 2004J. E. Gach Summary Concerns were raised in our department when four of our patients receiving PUVA treatment developed internal malignancy. We reviewed the medical and phototherapy case notes of patients who received either systemic or bath PUVA therapy in our department between 1986 and 1999. Among the 197 patients for whom we were able to trace the hospital records we identified five patients with internal malignancies. Over the same period (1986,1999) we calculated, using the Kaplan,Meier nonparametric estimator, that 4.6 cases of internal malignancy would have been anticipated in our study population. Therefore PUVA therapy did not appear to be a risk factor for internal malignancy. [source] Enrichment planting does not improve tree restoration when compared with natural regeneration in a former pine plantation in Kibale National Park, UgandaAFRICAN JOURNAL OF ECOLOGY, Issue 4 2009Patrick A. Omeja Abstract Given the high rates of deforestation and subsequent land abandonment, there are increasing calls to reforest degraded lands; however, many areas are in a state of arrested succession. Plantations can break arrested succession and the sale of timber can pay for restoration efforts. However, if the harvest damages native regeneration, it may be necessary to intervene with enrichment planting. Unfortunately, it is not clear when intervention is necessary. Here, we document the rate of biomass accumulation of planted seedlings relative to natural regeneration in a harvested plantation in Kibale National Park, Uganda. We established two 2-ha plots and in one, we planted 100 seedlings of each of four native species, and we monitored all tree regeneration in this area and the control plot. After 4 years, naturally regenerating trees were much taller, larger and more common than the planted seedlings. Species richness and two nonparametric estimators of richness were comparable between the plots. The cumulative biomass of planted seedlings accounted for 0.04% of the total above-ground tree biomass. The use of plantations facilitated the growth of indigenous trees, and enrichment planting subsequent to harvesting was not necessary to obtain a rich tree community with a large number of new recruits. Résumé Étant donné le rythme élevé de déforestation et, par la suite, d'abandon de terres, il y a des demandes croissantes pour repeupler les terrains dégradés; cependant, de nombreuses surfaces se trouvent dans un état de succession interrompu. Des plantations peuvent mettre fin à cette succession stoppée, et la vente de grumes peut financer les efforts de reforestation. Pourtant, si les prélèvements d'arbres endommagent la régénération naturelle, il peut être nécessaire d'intervenir avec des plantations d'appoint. Malheureusement, il n'est pas toujours facile de savoir quand une intervention est nécessaire. Nous documentons ici le taux d'accumulation de biomasse dans des jeunes arbres replantés par rapport à la régénération naturelle dans une plantation exploitée, à l'intérieur du Parc National de Kibale, en Ouganda. Nous avons établi deux parcelles de deux hectares et, dans une, nous avons repiqué 100 plants de chacune des quatre espèces natives. Nous avons ensuite suivi la régénération de tous les arbres dans cette parcelle et dans la parcelle témoin. Après quatre ans, les arbres provenant de la régénération naturelle étaient beaucoup plus grands, plus gros et plus abondants que les arbres replantés. La richesse en espèces et deux estimateurs nonparamétriques de la richesse étaient comparables dans les deux parcelles. La biomasse cumulée des jeunes arbres plantés comptait pour 0,04% de la biomasse aérienne totale des arbres. Le recours à des plantations a facilité la croissance d'arbres indigènes et la plantation d'appoint faisant suite à l'exploitation ne fut pas nécessaire pour obtenir une communauté d'arbres riche, avec un grand nombre de nouvelles recrues. [source] ESTIMATION AND HYPOTHESIS TESTING FOR NONPARAMETRIC HEDONIC HOUSE PRICE FUNCTIONSJOURNAL OF REGIONAL SCIENCE, Issue 3 2010Daniel P. McMillen ABSTRACT In contrast to the rigid structure of standard parametric hedonic analysis, nonparametric estimators control for misspecified spatial effects while using highly flexible functional forms. Despite these advantages, nonparametric procedures are still not used extensively for spatial data analysis due to perceived difficulties associated with estimation and hypothesis testing. We demonstrate that nonparametric estimation is feasible for large datasets with many independent variables, offering statistical tests of individual covariates and tests of model specification. We show that fixed parameterization of distance to the nearest rapid transit line is a misspecification and that pricing of access to this amenity varies across neighborhoods within Chicago. [source] Central limit theorems for nonparametric estimators with real-time random variablesJOURNAL OF TIME SERIES ANALYSIS, Issue 5 2010Tae Yoon Kim Primary 62G07; 62F12; Secondary 62M05 C13; C14 In this article, asymptotic theories for nonparametric methods are studied when they are applied to real-time data. In particular, we derive central limit theorems for nonparametric density and regression estimators. For this we formally introduce a sequence of real-time random variables indexed by a parameter related to fine gridding of time domain (or fine discretization). Our results show that the impact of fine gridding is greater in the density estimation case in the sense that strong dependence due to fine gridding severely affects the major strength of nonparametric density estimator (or its data-adaptive property). In addition, we discuss some issues about nonparametric regression model with fine gridding of time domain. [source] A bayesian estimator for the dependence function of a bivariate extreme-value distributionTHE CANADIAN JOURNAL OF STATISTICS, Issue 3 2008Simon Guillotte Abstract Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme-value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set. Un estimateur bayésien de la fonction de dépendance d'une loi des valeurs extrêmes bivariée Toute loi bivariée continue peut s'écrire en fonction de ses marges et d'une copule unique. Dans le cas des lois des valeurs extrêmes, la copule est caractérisée par une fonction de dépendance tandis que chaque marge dépend de trois paramètres. Les auteurs proposent une approche bayésienne pour l'estimation simultanée de la fonction de dépendance et des paramètres définissant les marges. Ils décrivent un modèle non paramétrique pour la fonction de dépendance et un algorithme MCMC à sauts réversibles pour le calcul de l'estimateur bayésien. Ils montrent par simulation que l'erreur quadratique moyenne intégrée de leur estimateur est plus faible que celle des estimateurs classiques, surtout dans de petits échantillons. Ils illustrent leur propos à l'aide de données hydrologiques. [source] Semiparametric Models for Cumulative Incidence FunctionsBIOMETRICS, Issue 1 2004John Bryant Summary. In analyses of time-to-failure data with competing risks, cumulative incidence functions may be used to estimate the time-dependent cumulative probability of failure due to specific causes. These functions are commonly estimated using nonparametric methods, but in cases where events due to the cause of primary interest are infrequent relative to other modes of failure, nonparametric methods may result in rather imprecise estimates for the corresponding subdistribution. In such cases, it may be possible to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimators. The cumulative incidence estimators so obtained are simple to compute and are considerably more efficient than the usual nonparametric estimator, particularly with regard to interpolation of cumulative incidence at early or intermediate time points within the range of data used to fit the function. More surprisingly, they are often nearly as efficient as fully parametric estimators. We illustrate the utility of this approach in the analysis of patients treated for early stage breast cancer. [source] Nonparametric Estimation for the Three-Stage Irreversible Illness,Death ModelBIOMETRICS, Issue 3 2000Somnath Datta Summary. In this paper, we present new nonparametric estimators of the stage-occupation probabilities in the three-stage irreversible illness-death model. These estimators use a fractional risk set and a reweighting approach and are valid under stage-dependent censoring. Using a simulated data set, we compare the behavior of our estimators with previously proposed estimators. We also apply our estimators to data on time to Pneumocystis pneumonia and death obtained from an AIDS cohort study. [source] | ||||||||||||||||