Showing papers on "Semiparametric model published in 2015"

Cox Proportional Hazards Regression Model

Abstract: The Cox proportional hazards model 132 is the most popular model for the analysis of survival data. It is a semiparametric model; it makes a parametric assumption concerning the effect of the predictors on the hazard function, but makes no assumption regarding the nature of the hazard function λ(t) itself. The Cox PH model assumes that predictors act multiplicatively on the hazard function but does not assume that the hazard function is constant (i.e., exponential model), Weibull, or any other particular form. The regression portion of the model is fully parametric; that is, the regressors are linearly related to log hazard or log cumulative hazard. In many situations, either the form of the true hazard function is unknown or it is complex, so the Cox model has definite advantages. Also, one is usually more interested in the effects of the predictors than in the shape of λ(t), and the Cox approach allows the analyst to essentially ignore λ(t), which is often not of primary interest.

Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game

Abstract: In this paper, we study the identification and estimation of a dynamic discrete game allowing for discrete or continuous state variables. We first provide a general nonparametric identification result under the imposition of an exclusion restriction on agent payoffs. Next we analyze large sample statistical properties of nonparametric and semiparametric estimators for the econometric dynamic game model. We also show how to achieve semiparametric efficiency of dynamic discrete choice models using a sieve based conditional moment framework. Numerical simulations are used to demonstrate the finite sample properties of the dynamic game estimators. An empirical application to the dynamic demand of the potato chip market shows that this technique can provide a useful tool to distinguish long term demand from short term demand by heterogeneous consumers.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique

Abstract: One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.

Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood

Abstract: We consider an independence feature screening technique for identifying explanatory variables that locally contribute to the response variable in high-dimensional regression analysis. Without requiring a specific parametric form of the underlying data model, our approach accommodates a wide spectrum of nonparametric and semiparametric model families. To detect the local contributions of explanatory variables, our approach constructs empirical likelihood locally in conjunction with marginal nonparametric regressions. Since our approach actually requires no estimation, it is advantageous in scenarios such as the single-index models where even specification and identification of a marginal model is an issue. By automatically incorporating the level of variation of the nonparametric regression and directly assessing the strength of data evidence supporting local contribution from each explanatory variable, our approach provides a unique perspective for solving feature screening problems. Theoretical analysis shows that our approach can handle data dimensionality growing exponentially with the sample size. With extensive theoretical illustrations and numerical examples, we show that the local independence screening approach performs promisingly.

An efficient semiparametric maxima estimator of the extremal index

Abstract: The extremal index θ, a measure of the degree of local dependence in the extremes of a stationary process, plays an important role in extreme value analyses. We estimate θ semiparametrically, using the relationship between the distribution of block maxima and the marginal distribution of a process to define a semiparametric model. We show that these semiparametric estimators are simpler and substantially more efficient than their parametric counterparts. We seek to improve efficiency further using maxima over sliding blocks. A simulation study shows that the semiparametric estimators are competitive with the leading estimators. An application to sea-surge heights combines inferences about θ with a standard extreme value analysis of block maxima to estimate marginal quantiles.

Moment Consistency of the Exchangeably Weighted Bootstrap for Semiparametric M-estimation

Abstract: The bootstrap variance estimate is widely used in semiparametric inferences. However, its theoretical validity is a well-known open problem. In this paper, we provide a first theoretical study on the bootstrap moment estimates in semiparametric models. Specifically, we establish the bootstrap moment consistency of the Euclidean parameter, which immediately implies the consistency of t-type bootstrap confidence set. It is worth pointing out that the only additional cost to achieve the bootstrap moment consistency in contrast with the distribution consistency is to simply strengthen the L1 maximal inequality condition required in the latter to the Lp maximal inequality condition for p≥1. The general Lp multiplier inequality developed in this paper is also of independent interest. These general conclusions hold for the bootstrap methods with exchangeable bootstrap weights, for example, non-parametric bootstrap and Bayesian bootstrap. Our general theory is illustrated in the celebrated Cox regression model.

A semiparametric approach for joint modeling of median and skewness

Abstract: We motivate this paper by showing through Monte Carlo simulation that ignoring the skewness of the response variable distribution in non-linear regression models may introduce biases on the parameter estimates and/or on the estimation of the associated variability measures. Then, we propose a semiparametric regression model suitable for data set analysis in which the distribution of the response is strictly positive and asymmetric. In this setup, both median and skewness of the response variable distribution are explicitly modeled, the median using a parametric non-linear function and the skewness using a semiparametric function. The proposed model allows for the description of the response using the log-symmetric distribution, which is a generalization of the log-normal distribution and is flexible enough to consider bimodal distributions in special cases as well as distributions having heavier or lighter tails than those of the log-normal one. An iterative estimation process as well as some diagnostic methods are derived. Two data sets previously analyzed under parametric models are reanalyzed using the proposed methodology.

Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models

Abstract: The conditional independence assumption for nonparametric multivariate finite mixture models, a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data, is the subject of an increasing number of theoretical and algorithmic developments in the statistical literature. After presenting a survey of this literature, including an in-depth discussion of the all-important identifiability results, this article describes and extends an algorithm for estimation of the parameters in these models. The algorithm works for any number of components in three or more dimensions. It possesses a descent property and can be easily adapted to situations where the data are grouped in blocks of conditionally independent variables. We discuss how to adapt this algorithm to various location-scale models that link component densities, and we even adapt it to a particular class of univariate mixture problems in which the components are assumed symmetric. We give a bandwidth selection procedure for our algorithm. Finally, we demonstrate the effectiveness of our algorithm using a simulation study and two psychometric datasets.

The Influence Function of Semiparametric Estimators

Abstract: Often semiparametric estimators are asymptotically equivalent to a sample average. The object being averaged is referred to as the in?uence function. The in?uence function is useful in formulating primitive regularity conditions for asymptotic normality, in efficiency comparions, for bias reduction, and for analyzing robustness. We show that the in?uence function of a semiparametric estimator can be calculated as the limit of the Gateaux derivative of a parameter with respect to a smooth deviation as the deviation approaches a point mass. We also consider high level and primitive regularity conditions for validity of the in?uence function calculation. The conditions involve Frechet differentiability, nonparametric convergence rates, stochastic equicontinuity, and small bias conditions. We apply these results to examples.

Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models.

Abstract: For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival models consistently perform well across a wide range of scenarios by adaptively balancing the strengths and weaknesses of individual candidate survival models. In addition, stacked survival models perform as well as or better than the model selected through cross-validation. Finally, stacked survival models are applied to a well-known German breast cancer study.

Alternated estimation in semi-parametric space-time branching-type point processes with application to seismic catalogs

Abstract: An estimation approach for the semi-parametric intensity function of a class of space-time point processes is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or an offspring is therefore estimated.

Variational inference for count response semiparametric regression

Abstract: Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e., a non-negative integer. We treat both the Poisson and Negative Binomial families as models for the response variable. Our approach utilizes recently developed methodology known as non-conjugate variational message passing. For concreteness, we focus on generalized additive mixed models, although our variational approximation approach extends to a wide class of semiparametric regression models such as those containing interactions and elaborate random effect structure.

FLP estimation of semi-parametric models for space–time point processes and diagnostic tools

Abstract: The conditional intensity function of a space–time branching model is defined by the sum of two main components: the long-run term intensity and short-run term one. Their simultaneous estimation is a complex issue that usually requires the use of hard computational techniques. This paper deals with a new mixed estimation approach for a particular space–time branching model, the Epidemic Type Aftershock Sequence model. This approach uses a simultaneous estimation of the different model components, alternating a parametric step for estimating the induced component by Maximum Likelihood and a non-parametric estimation step, for the background intensity, by FLP (Forward Predictive Likelihood). Moreover, proper graphical tools for diagnostics have been developed and collected, together with the used implemented code in a R package here introduced, named etasFLP .

Parametric and Semiparametric IV Estimation of Network Models with Selectivity

Abstract: We propose parametric and semiparametric IV estimators for spatial autoregressive models with network data where the network structure is endogenous. We embed a dyadic network formation process in the control function approach as in Heckman and Robb (1985). In the semiparametric case, we use power series to approximate the correction terms. We establish the consistency and asymptotic normality for both parametric and semiparametric cases. We also investigate their finite sample properties via Monte Carlo simulation.

Flexible estimation of a semiparametric two-component mixture model with one parametric component

Abstract: We study a two-component semiparametric mixture model where one component distribution belongs to a parametric class, while the other is symmetric but otherwise arbitrary. This semiparametric model has wide applications in many areas such as large-scale simultaneous testing/multiple testing, sequential clustering, and robust modeling. We develop a class of estimators that are surprisingly simple and are unique in terms of their construction. A unique feature of these methods is that they do not rely on the estimation of the nonparametric component of the model. Instead, the methods only require a working model of the unspecified distribution, which may or may not reflect the true distribution. In addition, we establish connections between the existing estimator and the new methods and further derive a semiparametric efficient estimator. We compare our estimators with the existing method and investigate the advantages and cost of the relatively simple estimation procedure.

Semiparametric Estimation of Partially Varying-Coefficient Dynamic Panel Data Models

Abstract: This paper studies a new class of semiparametric dynamic panel data models, in which some of coefficients are allowed to depend on other informative variables and some of the regressors can be endogenous. To estimate both parametric and nonparametric coefficients, a three-stage estimation method is proposed. A nonparametric generalized method of moments (GMM) is adopted to estimate all coefficients firstly and an average method is used to obtain the root-N consistent estimator of parametric coefficients. At the last stage, the estimator of varying coefficients is obtained by the partial residuals. The consistency and asymptotic normality of both estimators are derived. Monte Carlo simulations are conducted to verify the theoretical results and to demonstrate that the proposed estimators perform well in a finite sample.

Testing purchasing power parity hypothesis: a semiparametric varying coefficient approach

Abstract: Traditional linear cointegration tests of purchasing power parity (hereafter PPP) hypothesis often lead to rejection of the PPP hypothesis. More recent studies allowing for some sort of nonlinearity in econometric modelings (e.g., Michael et al. J Polit Econ 105:862–879, 1997) suggest mixed results and leave this problem as an unresolved issue. In this paper, we analyze PPP hypothesis within a semiparametric framework using the varying coefficient model with integrated variables as considered by Cai et al. (J Econ 148:101–113, 2009) and Xiao (J Econ 152:81–92, 2009). Applying the cointegration test suggested by Xiao (J Econ 152:81–92, 2009), we conduct the cointegration test of PPP hypothesis between US and Canada, US and Japan, and US and UK, respectively. In contrast to the usual findings based on linear model PPP hypothesis testing, our semiparametric model-based tests support the PPP hypothesis.

A Cox-Aalen Model for Interval-censored Data

Abstract: This is the peer reviewed version of the following article: Boruvka, A., and Cook, R. J. (2015), A Cox-Aalen Model for Interval-censored Data. Scand J Statist, 42, 414–426. doi: 10.1111/sjos.12113., which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/sjos.12113/full. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.