An Introduction to Bootstrap Methods with Applications to R

... including spatial data analysis, P-value adjustment in multiple testing, censored data, subset selection in regression models, process capability indices, and some new material on bioequivalence and covariate adjustment to area under the curve for receiver operating characteristics for diagnostic te ...

... including spatial data analysis, P-value adjustment in multiple testing, censored data, subset selection in regression models, process capability indices, and some new material on bioequivalence and covariate adjustment to area under the curve for receiver operating characteristics for diagnostic te ...

Robust Confidence Intervals in Nonlinear Regression under Weak Identification

... limit operation, only a pointwise result is obtained. A pointwise result does not provide a good approximation to the …nite-sample size because the contamination on the …nite-sample behavior by non-identi…cation exists for any given sample size n. Only a uniform result can capture that the extent o ...

... limit operation, only a pointwise result is obtained. A pointwise result does not provide a good approximation to the …nite-sample size because the contamination on the …nite-sample behavior by non-identi…cation exists for any given sample size n. Only a uniform result can capture that the extent o ...

1.14 Polynomial regression

... A quite flexible class of models for the mean of a real valued random variable X given a real valued covariate y is EX = β0 + β1 y + β2 y 2 + . . . + βd y d , thus the mean is a d’th order polynomial in the covariate y. Let y1 , . . . , yn be given, real numbers – the covariates – and Xi = β 0 + β 1 ...

... A quite flexible class of models for the mean of a real valued random variable X given a real valued covariate y is EX = β0 + β1 y + β2 y 2 + . . . + βd y d , thus the mean is a d’th order polynomial in the covariate y. Let y1 , . . . , yn be given, real numbers – the covariates – and Xi = β 0 + β 1 ...

Vector Autoregressions with Parsimoniously Time Varying

... In the models discussed above the parameters do vary at every point in time; another strand of literature investigates models with a finite number of changes in the parameters, or a finite number of possible values the parameters may take over time. One example of such models is regime switching mod ...

... In the models discussed above the parameters do vary at every point in time; another strand of literature investigates models with a finite number of changes in the parameters, or a finite number of possible values the parameters may take over time. One example of such models is regime switching mod ...

Vector Autoregressions with Parsimoniously Time

... In the models discussed above the parameters do vary at every point in time; another strand of literature investigates models with a finite number of changes in the parameters, or a finite number of possible values the parameters may take over time. One example of such models is regime switching mod ...

... In the models discussed above the parameters do vary at every point in time; another strand of literature investigates models with a finite number of changes in the parameters, or a finite number of possible values the parameters may take over time. One example of such models is regime switching mod ...

ORACLE INEQUALITIES FOR HIGH DIMENSIONAL

... t is assumed to be a sequence of i.i.d. error terms with an NkT (0, Σ) distribution. Notice that the number of variables as well as the number of lags is indexed by T indicating that both of these are allowed to increase as the sample size increases – and in particular may be larger than T . Equati ...

... t is assumed to be a sequence of i.i.d. error terms with an NkT (0, Σ) distribution. Notice that the number of variables as well as the number of lags is indexed by T indicating that both of these are allowed to increase as the sample size increases – and in particular may be larger than T . Equati ...

Pivotal Estimation via Square-root Lasso in

... normality results of [8, 3] on a rather specific linear model to a generic nonlinear problem, which covers covers smooth frameworks in statistics and in econometrics, where the main parameters of interest are defined via nonlinear instrumental variable/moment conditions or z-conditions containing un ...

... normality results of [8, 3] on a rather specific linear model to a generic nonlinear problem, which covers covers smooth frameworks in statistics and in econometrics, where the main parameters of interest are defined via nonlinear instrumental variable/moment conditions or z-conditions containing un ...

Download paper (PDF)

... similar to the one of causal inference. Essentially, we can view the treatment indicator as an indicator for outcome response and under the assumption that the outcome is conditionally independent of the response (or treatment) indicator given the observed covariates (specifically, under the strong ...

... similar to the one of causal inference. Essentially, we can view the treatment indicator as an indicator for outcome response and under the assumption that the outcome is conditionally independent of the response (or treatment) indicator given the observed covariates (specifically, under the strong ...

Pivotal Estimation via Square-root Lasso in Nonparametric

... normality results of [8, 3] on a rather specific linear model to a generic nonlinear problem, which covers covers smooth frameworks in statistics and in econometrics, where the main parameters of interest are defined via nonlinear instrumental variable/moment conditions or z-conditions containing un ...

... normality results of [8, 3] on a rather specific linear model to a generic nonlinear problem, which covers covers smooth frameworks in statistics and in econometrics, where the main parameters of interest are defined via nonlinear instrumental variable/moment conditions or z-conditions containing un ...

The White test for heteroskedasticity

... If errors are homoskedastic at the individual-level, WLS with weights equal to firm size mi should be used. If the assumption of homoskedasticity at the individual-level is not exactly right, one can calculate robust standard errors after WLS (i.e. for the transformed model). ...

... If errors are homoskedastic at the individual-level, WLS with weights equal to firm size mi should be used. If the assumption of homoskedasticity at the individual-level is not exactly right, one can calculate robust standard errors after WLS (i.e. for the transformed model). ...

How Much Should We Trust Estimates from Multiplicative Interaction

... support in the data. For this we can simply compare the distribution of X in both groups and examine the range of X values for which there is a sufficient number of data points in both groups. In our example, we see that we see that both groups share a common support of X for the range between abou ...

... support in the data. For this we can simply compare the distribution of X in both groups and examine the range of X values for which there is a sufficient number of data points in both groups. In our example, we see that we see that both groups share a common support of X for the range between abou ...

Bayesian Variable Selection in Normal Regression Models

... The estimator β̂ OLS has many desirable properties, it is unbiased and the efficient estimator in the class of linear unbiased estimators, i.e. it has the so called BLUE-property (Gauß Markov theorem). ...

... The estimator β̂ OLS has many desirable properties, it is unbiased and the efficient estimator in the class of linear unbiased estimators, i.e. it has the so called BLUE-property (Gauß Markov theorem). ...

On the impact of financial distress on capital structure

... for ﬁnancial distress costs. These studies ﬁnd no evidence of a negative relationship between ﬁnancial distress costs and leverage. Several other studies that investigate the relationship between leverage and ﬁnancial distress costs do so incorporating ﬁrm size as an inverse proxy for expected ﬁnanc ...

... for ﬁnancial distress costs. These studies ﬁnd no evidence of a negative relationship between ﬁnancial distress costs and leverage. Several other studies that investigate the relationship between leverage and ﬁnancial distress costs do so incorporating ﬁrm size as an inverse proxy for expected ﬁnanc ...

Z - NYU

... Asymptotic efficiency of the IV estimator. The variance is larger than that of LS. (A large sample type of GaussMarkov result is at work.) (1) It’s a moot point. LS is inconsistent. (2) Mean squared error is uncertain: MSE[estimator|β]=Variance + square of bias. IV may be better or worse. Depends on ...

... Asymptotic efficiency of the IV estimator. The variance is larger than that of LS. (A large sample type of GaussMarkov result is at work.) (1) It’s a moot point. LS is inconsistent. (2) Mean squared error is uncertain: MSE[estimator|β]=Variance + square of bias. IV may be better or worse. Depends on ...

Econometrics-I-12

... Asymptotic efficiency of the IV estimator. The variance is larger than that of LS. (A large sample type of GaussMarkov result is at work.) (1) It’s a moot point. LS is inconsistent. (2) Mean squared error is uncertain: MSE[estimator|β]=Variance + square of bias. ...

... Asymptotic efficiency of the IV estimator. The variance is larger than that of LS. (A large sample type of GaussMarkov result is at work.) (1) It’s a moot point. LS is inconsistent. (2) Mean squared error is uncertain: MSE[estimator|β]=Variance + square of bias. ...

Another look at the jackknife: further examples of generalized

... Liu and Singh (1992). The first two are modifications to the usual jackknives, adapted to suit regression data of unbalanced nature. Liu and Singh's weighted jackknife came up as an example of a E-class resampling technique. The asymptotic properties of these weighted jackknives are comparable to th ...

... Liu and Singh (1992). The first two are modifications to the usual jackknives, adapted to suit regression data of unbalanced nature. Liu and Singh's weighted jackknife came up as an example of a E-class resampling technique. The asymptotic properties of these weighted jackknives are comparable to th ...

Chapter 13 Estimation and Evidence in Forensic Anthropology

... agrees with the estimate of 1494 mm from “inverse calibration” and standard deviation of 41.10 obtainable from that of Konigsberg et al. ([20] Table 4). Using an uninformative prior, the WinBUGS estimated stature is 1424 mm, with a standard deviation of 46.59, which again agrees with the estimate fr ...

... agrees with the estimate of 1494 mm from “inverse calibration” and standard deviation of 41.10 obtainable from that of Konigsberg et al. ([20] Table 4). Using an uninformative prior, the WinBUGS estimated stature is 1424 mm, with a standard deviation of 46.59, which again agrees with the estimate fr ...

Weighted Quantile Regression for Analyzing Health Care Cost Data

... approach may lead to biased estimation. The quantile regression based weighting approach we study in this paper is semiparametric and circumvents the difficulty of specifying the joint or conditional likelihood function. In particular, it requires no parametric distributional assumptions for either ...

... approach may lead to biased estimation. The quantile regression based weighting approach we study in this paper is semiparametric and circumvents the difficulty of specifying the joint or conditional likelihood function. In particular, it requires no parametric distributional assumptions for either ...

Robust Standard Errors for Panel Regressions with Cross

... model’s assumptions are violated, it is common to rely on “robust” standard errors. Probably the most popular of these alternative covariance matrix estimators has been developed by Huber (1967), Eicker (1967), and White (1980). Provided that the residuals are independently distributed, standard err ...

... model’s assumptions are violated, it is common to rely on “robust” standard errors. Probably the most popular of these alternative covariance matrix estimators has been developed by Huber (1967), Eicker (1967), and White (1980). Provided that the residuals are independently distributed, standard err ...

Teeter, Rebecca Ann; (1982)Effects of Measurement Error in Piecewise Regression Models."

... In regression problems one assumes that the measured Y values vary from their true values only by a random error component and one can assume that the X values are known constants or that X is a random variable whose distribution does not involve the regression parameters. With either of these assum ...

... In regression problems one assumes that the measured Y values vary from their true values only by a random error component and one can assume that the X values are known constants or that X is a random variable whose distribution does not involve the regression parameters. With either of these assum ...

Econometrics-I-18

... distributional assumptions. They are very dependent on the particular assumptions. The oft cited disadvantage of their mediocre small sample properties is overstated in view of the usual paucity of viable alternatives. ...

... distributional assumptions. They are very dependent on the particular assumptions. The oft cited disadvantage of their mediocre small sample properties is overstated in view of the usual paucity of viable alternatives. ...

PDF

... cases: 1) the mismeasured variable is assumed exogenous, and no instruments are available; 2) the mismeasured variable is assumed exogenous, and one or more instruments are available; and 3) the mismeasured variable is not assumed exogenous, and instruments are available. In the first two cases, we ...

... cases: 1) the mismeasured variable is assumed exogenous, and no instruments are available; 2) the mismeasured variable is assumed exogenous, and one or more instruments are available; and 3) the mismeasured variable is not assumed exogenous, and instruments are available. In the first two cases, we ...

Bias Uncertainty - Integrated Sciences Group

... We now include parameter drift. Suppose that the perfume experiment is conducted in a tunnel, and that there is a constant air flow present. In this case, perfume molecules again exhibit random Brownian motion, but there is also a systematic motion due to the air flow. If we compare perfume aroma ar ...

... We now include parameter drift. Suppose that the perfume experiment is conducted in a tunnel, and that there is a constant air flow present. In this case, perfume molecules again exhibit random Brownian motion, but there is also a systematic motion due to the air flow. If we compare perfume aroma ar ...

STATISTICS 1

... The box-and-whiskers plot in the upper left depicts the distribution of data. The box denotes the part of the data that lies between the lower q(0.25) and upper q(0.75) quartiles (quartiles are explained below). Inside the box there is also a vertical line denoting the sample median (see next page). ...

... The box-and-whiskers plot in the upper left depicts the distribution of data. The box denotes the part of the data that lies between the lower q(0.25) and upper q(0.75) quartiles (quartiles are explained below). Inside the box there is also a vertical line denoting the sample median (see next page). ...