Regression functions for tower spray driers

The approach used by Cunia to combine the error from sample plots with the error from volume or biomass tables when Continuous Forest Inventory (CFI) estimates of current values and growth are calculated is extended to the CFI systems using Sampling with Partial Replacement (SPR). The formulae are derived for the case of SPR on two measurement occasions when (i) volume or biomass tables are constructed from linear regressions for which an estimate of the covariance matrix of the regression coefficients is known, and (ii) the sample plots or points are selected by random sampling independently of the given volume or biomass regression functions.

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KTBoost: Combined Kernel and Tree Boosting

Neural Processing Letters ◽

10.1007/s11063-021-10434-9 ◽

2021 ◽

Author(s):

Fabio Sigrist

Keyword(s):

Hilbert Space ◽

Reproducing Kernel ◽

Predictive Accuracy ◽

Reproducing Kernel Hilbert Space ◽

Regression Tree ◽

Regression Function ◽

Data Sets ◽

Regression Functions ◽

Boosting Algorithm

AbstractWe introduce a novel boosting algorithm called ‘KTBoost’ which combines kernel boosting and tree boosting. In each boosting iteration, the algorithm adds either a regression tree or reproducing kernel Hilbert space (RKHS) regression function to the ensemble of base learners. Intuitively, the idea is that discontinuous trees and continuous RKHS regression functions complement each other, and that this combination allows for better learning of functions that have parts with varying degrees of regularity such as discontinuities and smooth parts. We empirically show that KTBoost significantly outperforms both tree and kernel boosting in terms of predictive accuracy in a comparison on a wide array of data sets.

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Higher order regression functions result better fit for the calibration curve

Journal of Orthopaedic Research® ◽

10.1002/jor.22347 ◽

2013 ◽

Vol 31 (7) ◽

pp. 1164-1164

Author(s):

Ibrahim Mutlu ◽

Yasin Kisioglu

Keyword(s):

Calibration Curve ◽

Higher Order ◽

Regression Functions

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Nonparametric Density and Regression Estimation

The Journal of Economic Perspectives ◽

10.1257/jep.15.4.11 ◽

2001 ◽

Vol 15 (4) ◽

pp. 11-28 ◽

Cited By ~ 109

Author(s):

John DiNardo ◽

Justin L Tobias

Keyword(s):

Functional Form ◽

Regression Function ◽

Nonparametric Methods ◽

Actual Data ◽

Attractive Alternative ◽

Regression Estimation ◽

Parametric Methods ◽

Regression Functions

We provide a nontechnical review of recent nonparametric methods for estimating density and regression functions. The methods we describe make it possible for a researcher to estimate a regression function or density without having to specify in advance a particular--and hence potentially misspecified functional form. We compare these methods to more popular parametric alternatives (such as OLS), illustrate their use in several applications, and demonstrate their flexibility with actual data and generated-data experiments. We show that these methods are intuitive and easily implemented, and in the appropriate context may provide an attractive alternative to “simpler” parametric methods.

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Optimal designs for nonparametric estimation of zeros of regression functions

Tatra Mountains Mathematical Publications ◽

10.2478/v10127-012-0006-z ◽

2012 ◽

Vol 51 (1) ◽

pp. 55-65

Author(s):

Zdeněk Hlávka

Keyword(s):

Confidence Interval ◽

Nonparametric Estimation ◽

Regression Function ◽

Optimal Designs ◽

Nonparametric Estimators ◽

Regression Functions ◽

Expected Width

ABSTRACT We investigate nonparametric estimators of zeros of a regression function and its derivatives and we derive the distribution of design points minimizing the expected width of a confidence interval and the expected variance of the proposed estimator.

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NONPARAMETRIC IDENTIFICATION OF ACCELERATED FAILURE TIME COMPETING RISKS MODELS

Econometric Theory ◽

10.1017/s0266466612000795 ◽

2013 ◽

Vol 29 (5) ◽

pp. 905-919 ◽

Cited By ~ 13

Author(s):

Sokbae Lee ◽

Arthur Lewbel

Keyword(s):

Competing Risks ◽

Failure Time ◽

Nonparametric Identification ◽

Accelerated Failure Time ◽

Rank Condition ◽

Survivor Function ◽

Auction Models ◽

Regression Functions ◽

Accelerated Failure ◽

Identification Strategy

We provide new conditions for identification of accelerated failure time competing risks models. These include Roy models and some auction models. In our setup, unknown regression functions and the joint survivor function of latent disturbance terms are all nonparametric. We show that this model is identified given covariates that are independent of latent errors, provided that a certain rank condition is satisfied. We present a simple example in which our rank condition for identification is verified. Our identification strategy does not depend on identification at infinity or near zero, and it does not require exclusion assumptions. Given our identification, we show estimation can be accomplished using sieves.

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ANALYSIS OF THE TOPOGRAPHIC DISTRIBUTION OF THYROID ACTIVITY IN A TELEOST FISH: CARASSIUS CARASSIUS L.

Acta Endocrinologica ◽

10.1530/acta.0.0560533 ◽

1967 ◽

Vol 56 (3) ◽

pp. 533-546 ◽

Cited By ~ 4

Author(s):

Lars Frisén ◽

Marianne Frisén

Keyword(s):

Crucian Carp ◽

Carassius Carassius ◽

Caudal Part ◽

Thyroid Activity ◽

X Ray ◽

Lower Jaw ◽

Thyroid Uptake ◽

Regression Functions ◽

Sampling Intervals ◽

High Degree

ABSTRACT Combined autoradiography and X-ray photography has demonstrated that the tissues of the caudal part of the lower jaw and the pronephric region have a considerably larger uptake of radioiodine than other tissues in the immature crucian carp, Carassius carassius L. It is known from previous studies that thyroid follicles occur in these regions. The pronephric thyroid differs from the mandibular thyroid with regard to radioiodine turnover. On an average, the pronephric thyroid has a larger uptake than the lower jaw thyroid. The relative uptakes varied considerably in the population investigated. The ratio mandibular uptake/total thyroid uptake has been determined at several sampling intervals. The means of these ratios show no significant trend with time, a finding which strongly supports the view that the two regions are variably sized parts of a physiologically homogeneous thyroid gland. The analysis is based on a mathematical model of iodine turnover. The method of comparing complex regression functions (sums of exponential functions) should be widely applicable. The correlation between the radioiodine content of either region and total thyroid content is low. Thus, it is necessary to study simultaneously all follicle-carrying tissues in fish with more than one aggregation of thyroid elements. The net radioiodine turnover in the crucian carp indicates a comparatively high degree of thyroid activity and considerably higher than that reported for C. auratus L. This phenomenon is discussed.

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ASYMPTOTICALLY EFFICIENT ESTIMATION OF WEIGHTED AVERAGE DERIVATIVES WITH AN INTERVAL CENSORED VARIABLE

Econometric Theory ◽

10.1017/s0266466616000384 ◽

2016 ◽

Vol 33 (5) ◽

pp. 1218-1241 ◽

Cited By ~ 3

Author(s):

Hiroaki Kaido

Keyword(s):

Support Function ◽

Weighted Average ◽

Interval Data ◽

Efficient Estimation ◽

Outcome Variable ◽

Nonparametric Bounds ◽

Regression Functions ◽

Semiparametric Efficiency Bound ◽

Derivatives Of ◽

Interval Valued

This paper studies the identification and estimation of weighted average derivatives of conditional location functionals including conditional mean and conditional quantiles in settings where either the outcome variable or a regressor is interval-valued. Building on Manski and Tamer (2002, Econometrica 70(2), 519–546) who study nonparametric bounds for mean regression with interval data, we characterize the identified set of weighted average derivatives of regression functions. Since the weighted average derivatives do not rely on parametric specifications for the regression functions, the identified set is well-defined without any functional-form assumptions. Under general conditions, the identified set is compact and convex and hence admits characterization by its support function. Using this characterization, we derive the semiparametric efficiency bound of the support function when the outcome variable is interval-valued. Using mean regression as an example, we further demonstrate that the support function can be estimated in a regular manner by a computationally simple estimator and that the efficiency bound can be achieved.

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