Naive Estimates Research Articles

Frequency Shift and Sub-band Effect in Pair-Production Process Under Adiabatic Closing the External Field

Oscillating electric field is chosen to investigate the electron-positron pair production process by using a quantum kinetic theory and the effective mass model [Phys. Rev. Lett. 112, 050402 (2014)]. The particle yield exhibits a characteristic oscillatory structure which is related to the multi-photon thresholds. The true peak positions are typically slightly above the naive threshold estimate, which is defined as frequency shift. During the numerical calculations, we find the frequency shift can be affected by the system parameters under adiabatic closing the external field, it is worthwhile to study in detail. In this paper, we investigate the frequency shift and the sub-band effect in electron-positron pair production with oscillating electric field. First, a quantum kinetic theory and the effective mass are presented to obtain the frequency shift, the results are fitted very well. And we find the frequency shift and the sub-band effect can be influenced by pulse duration, photon number, and strength of the external field. The frequency shift becomes evident as increases of photon number and the external field strength. The sub-band width is relatively lower at longer pulse duration, higher photon number region, and weaker external field. The results shown in the paper are helpful for understanding multi-photon pair production process in the strong field.

Thermodynamical aspects of running vacuum models

The thermal history of a large class of running vacuum models in which the effective cosmological term is described by a truncated power series of the Hubble rate, whose dominant term is $\Lambda (H) \propto H^{n+2}$, is discussed in detail. Specifically, by assuming that the ultra-relativistic particles produced by the vacuum decay emerge into space-time in such a way that its energy density $\rho_r \propto T^{4}$, the temperature evolution law and the increasing entropy function are analytically calculated. For the whole class of vacuum models explored here we findthat the primeval value of the comoving radiation entropy density (associated to effectively massless particles) starts from zero and evolves extremely fast until reaching a maximum near the end of the vacuum decay phase, where it saturates. The late time conservation of the radiation entropy during the adiabatic FRW phase also guarantees that the whole class of running vacuum models predicts thesame correct value of the present day entropy, $S_{0} \sim 10^{87-88}$ (in natural units), independently of the initial conditions. In addition, by assuming Gibbons-Hawking temperature as an initial condition, we find that the ratio between the late time and primordial vacuum energy densities is in agreement with naive estimates from quantum field theory, namely, $\rho_{\Lambda 0}/\rho_{\Lambda I} \sim10^{-123}$. Such results are independent on the power $n$ and suggests that the observed Universe may evolve smoothly between two extreme, unstable, nonsingular de Sitter phases.

THE EFFICIENT COMPUTATION AND THE SENSITIVITY ANALYSIS OF FINITE-TIME RUIN PROBABILITIES AND THE ESTIMATION OF RISK-BASED REGULATORY CAPITAL

AbstractSolvency regulations require financial institutions to hold initial capital so that ruin is a rare event. An important practical problem is to estimate the regulatory capital so the ruin probability is at the regulatory level, typically with less than 0.1% over a finite-time horizon. Estimating probabilities of rare events is challenging, since naive estimations via direct simulations of the surplus process is not feasible. In this paper, we present a stratified sampling algorithm for estimating finite-time ruin probabilities. We further introduce a sequence of measure changes to remove the pathwise discontinuities of the estimator, and compute unbiased first and second-order derivative estimates of the finite-time ruin probabilities with respect to both distributional and structural parameters. We then estimate the regulatory capital and its sensitivities. These estimates provide information to insurance companies for meeting prudential regulations as well as designing risk management strategies. Numerical examples are presented for the classical model, the Sparre Andersen model with interest and the periodic risk model with interest to demonstrate the speed and efficacy of our methodology.

On optimal smoothing of density estimators obtained from orthogonal polynomial expansion methods

We discuss the application of orthogonal polynomials to the estimation of probability density functions, particularly with regard to accessing features of a portfolio's profit/loss distribution. Such expansions are given by the sum of known orthogonal polynomials multiplied by an associated weight function. However, naive applications of expansion methods are flawed. The shape of the estimator's tail can undulate under the influence of the constituent polynomials in the expansion, and it can even exhibit regions of negative density. This paper presents techniques to remedy these flaws and improve the quality of risk estimation.We show that by targeting a smooth density that is sufficiently close to the target density, we can obtain expansion-based estimators that do not have the shortcomings of equivalent naive estimators. In particular, we apply optimization and smoothing techniques that place greater weight on the tails than on the body of the distribution. Numerical examples using both real and simulated data illustrate our approach. We further outline how our techniques can apply to a wide class of expansion methods and indicate opportunities to extend to the multivariate case, where distributions of individual component risk factors in a portfolio can be accessed for the purpose of risk management.

Spatial regression with covariate measurement error: A semiparametric approach.

Spatial data have become increasingly common in epidemiology and public health research thanks to advances in GIS (Geographic Information Systems) technology. In health research, for example, it is common for epidemiologists to incorporate geographically indexed data into their studies. In practice, however, the spatially defined covariates are often measured with error. Naive estimators of regression coefficients are attenuated if measurement error is ignored. Moreover, the classical measurement error theory is inapplicable in the context of spatial modeling because of the presence of spatial correlation among the observations. We propose a semiparametric regression approach to obtain bias-corrected estimates of regression parameters and derive their large sample properties. We evaluate the performance of the proposed method through simulation studies and illustrate using data on Ischemic Heart Disease (IHD). Both simulation and practical application demonstrate that the proposed method can be effective in practice.

Thresholding least-squares inference in high-dimensional regression models

We propose a thresholding least-squares method of inference for high-dimensional regression models when the number of parameters, $p$, tends to infinity with the sample size, $n$. Extending the asymptotic behavior of the F-test in high dimensions, we establish the oracle property of the thresholding least-squares estimator when $p=o(n)$. We propose two automatic selection procedures for the thresholding parameter using Scheffe and Bonferroni methods. We show that, under additional regularity conditions, the results continue to hold even if $p=\exp(o(n))$. Lastly, we show that, if properly centered, the residual-bootstrap estimator of the distribution of thresholding least-squares estimator is consistent, while a naive bootstrap estimator is inconsistent. In an intensive simulation study, we assess the finite sample properties of the proposed methods for various sample sizes and model parameters. The analysis of a real world data set illustrates an application of the methods in practice.

Childhood Housing and Adult Earnings: A Between-Siblings Analysis of Housing Vouchers and Public Housing

We create a national-level longitudinal data set to analyze how children’s participation in public and voucher-assisted housing affects age 26 earnings and adult incarceration. Naive OLS estimates suggest that returns to subsidized housing participation are negative, but that relationship is driven by household selection into assisted housing. Household fixed-effects estimates indicate that additional years of public housing and voucher-assisted housing increase adult earnings by 4.9% and 4.7% for females and 5.1% and 2.6% for males, respectively. Childhood participation in assisted housing also reduces the likelihood of adult incarceration for males and females from all household race/ethnicity groups.

Controlling for the Compromise Effect Debiases Estimates of Risk Preference Parameters

The compromise effect arises when options near the of a choice set are more appealing. The compromise effect poses conceptual and practical problems for economic research: by influencing choices, it distorts revealed preferences, biasing researchers' inferences about deep (i.e., domain general) preferences. We propose and estimate an econometric model that disentangles and identifies both deep preferences and the context-dependent compromise effect. We demonstrate our method using data from an experiment with 550 participants who made choices over lotteries from multiple price lists. Following prior work, we manipulate the compromise effect by varying the middle options of each multiple price list and then estimate risk preferences without modelling the compromise effect. These naive parameter estimates are not robust: they change as the compromise effect is manipulated. To eliminate this bias, we incorporate the compromise effect directly into our econometric model. We show that this method generates robust estimates of risk preference parameters that are no longer sensitive to compromise-effect manipulations. This method can be applied to other settings that exhibit the compromise effect.

Childhood Housing and Adult Earnings: A Between-Siblings Analysis of Housing Vouchers and Public Housing

To date, research on the long-term effects of childhood participation in voucher-assisted and public housing has been limited by the lack of data and suitable identification strategies. We create a national level longitudinal data set that enables us to analyze how children’s housing experiences affect adult earnings and incarceration rates. While naive estimates suggest there are substantial negative consequences to childhood participation in voucher assisted and public housing, this result appears to be driven largely by selection of households into housing assistance programs. To mitigate this source of bias, we employ household fixed-effects specifications that use only within-household (across-sibling) variation for identification. Compared to naive specifications, household fixed-effects estimates for earnings are universally more positive, and they suggest that there are positive and statistically significant benefits from childhood residence in assisted housing on young adult earnings for nearly all demographic groups. Childhood participation in assisted housing also reduces the likelihood of incarceration across all household race/ethnicity groups. Time spent in voucher-assisted or public housing is especially beneficial for females from non-Hispanic Black households, who experience substantial increases in expected earnings and lower incarceration rates.

Environmental Drivers of Occupancy and Detection of Olympic Mudminnow

AbstractThe Olympic Mudminnow Novumbra hubbsi is a highly endemic freshwater fish found only in Washington State, where their distribution is limited to low‐elevation wetland habitats. The distributional extent of the Olympic Mudminnow is well established, but local and watershed environmental features associated with their presence or absence within the range are poorly understood, making it difficult to determine habitat needs versus availability. We surveyed 22 sites in 2 years along the Chehalis River with the objective of modeling environmental characteristics associated with occupancy by Olympic Mudminnows, while also accounting for incomplete detection. Occupancy and detection probabilities were highly similar between years, and occupancy that incorporated detection probabilities was 47% higher than naive estimates in a given year. Modeling with environmental covariates supported the importance of low temperatures for predicting the occurrence of Olympic Mudminnows at sites, and detection within sites was associated most strongly with shallow depths and low dissolved oxygen. These results are consistent with prior research indicating the preferential use of groundwater springs by Olympic Mudminnows, particularly in warmer summer months. Our research expands the existing knowledge of Olympic Mudminnow distributions by documenting main‐stem‐oriented populations at varying levels of abundance and suggesting habitat features that may increase occupancy and detection probabilities. The sampling and modeling approach we describe also informs development of standardized survey protocols for Olympic Mudminnows, helping to optimize resources for monitoring occupancy and abundance across their limited range.

Estimation ofMuscardinus avellanariuspopulation density by live-trapping

Common dormouse (Muscardinus avellanarius) density in Transylvanian Plain is investigated using live-traps. Estimated population size is 39 individuals. Results using non-spatial methods combined with ad hoc calculations of the effective trapping area overestimated common dormouse density, both when using the naive density estimation (27 ind./ha) and also when the “edge-effect” was accounted for by the addition of a boundary strip (16 ind./ha). Compared with published results using the same methods, our results are yet significantly higher. Spatially explicit capture-recapture approach yields lower density, of 13 ind./ha (maximum likelihood estimate), but still one of the highest densities reported for the species. Interspecific competition for traps was negligible at our study site.

Generalizing visual fast count estimators for underwater video surveys

A generalized Visual Fast Count (GVFC) method of moments estimator is proposed for estimating epifaunal species abundance from underwater video survey transects. This formalises and provides a statistical framework for previous ad‐hoc Visual Fast Count methods. For a single transect, we derive the expected value of the naive GVFC estimator and use this to create the method of moments estimator, which has reduced bias. A maximum likelihood estimator for multiple transects is derived. For illustration, our methods are applied to a series of video trawls at Folkestone Pomerania in the Dover Straits, UK. Although our methods have been developed for marine applications, they could also be applied to some terrestrial transect surveys.

Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data.

Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience. The “common input” problem presents a major roadblock: it is difficult to reliably distinguish causal connections between pairs of observed neurons versus correlations induced by common input from unobserved neurons. Available techniques allow us to simultaneously record, with sufficient temporal resolution, only a small fraction of the network. Consequently, naive connectivity estimators that neglect these common input effects are highly biased. This work proposes a “shotgun” experimental design, in which we observe multiple sub-networks briefly, in a serial manner. Thus, while the full network cannot be observed simultaneously at any given time, we may be able to observe much larger subsets of the network over the course of the entire experiment, thus ameliorating the common input problem. Using a generalized linear model for a spiking recurrent neural network, we develop a scalable approximate expected loglikelihood-based Bayesian method to perform network inference given this type of data, in which only a small fraction of the network is observed in each time bin. We demonstrate in simulation that the shotgun experimental design can eliminate the biases induced by common input effects. Networks with thousands of neurons, in which only a small fraction of the neurons is observed in each time bin, can be quickly and accurately estimated, achieving orders of magnitude speed up over previous approaches.

Smoothed and Corrected Score Approach to Censored Quantile Regression With Measurement Errors

Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and corrected estimating equations to obtain consistent estimators. We establish consistency and asymptotic normality for the proposed estimators of quantile regression coefficients. Compared with the naive estimator, the proposed method can eliminate the estimation bias under various measurement error distributions and model error distributions. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a lung cancer study. Supplementary materials for this article are available online.

An oasis in the desert: The potential of water sources as camera trap sites in arid environments for surveying a carnivore guild

Discussions regarding the importance of accounting for detection probability have long been present in ecological literature. Various studies have demonstrated the influence of survey design on detection probabilities, and whilst the placement of camera traps along roads is a commonly used survey design, it has shown to be biased towards certain species. In arid environments, water sources have the potential to be efficient sites for camera trap placement. We compared the influence of a water source camera trap survey design on the detection probabilities of a guild of seven carnivore species, in comparison detection probabilities from camera traps along roads, on arid, commercial farmland in southern Namibia. Results showed detection probabilities for all species to be higher at water, with the water source design producing shorter latencies of detections and higher naive occupancy estimates for most species. However, for species with unique markings, the water source design produced lower proportions of images suitable for individual identification. As detection probabilities of all species were influenced in a positive manner, we suggest placing camera traps at water sources in arid environments to be an effective survey design. However, for surveys requiring individual identification, placing camera traps along roads may be more suitable.

Estimating occupancy and abundance of caracal in a semi-arid habitat, Western India

We estimated the occupancy and abundance of caracal (Felis caracal) using camera traps in the core area of Ranthambhore Tiger Reserve (RTR), a semi-arid habitat, in Western India, from 2006 to 2009. Caracals were detected in 17 of 28 grids, yielding a naive occupancy estimate of 0.61. After accounting for imperfect detection (P = 0.17 ± 0.04 [SE]), the proportion of sites occupied (occupancy) was 87 %. Caracal abundance was ∼28.17 ± 3.02 individuals in the study area. Based on the total area available for caracals (588 km2), the average expected density was as 4.8 caracals per 100 km2. Information on caracal occupancy and abundance in the Indian sub-continent has not been documented or published, and this is the first attempt to estimate occupancy rates and the approximate population size of the caracal in RTR. Hence, our data should be used as baseline information for making future management and conservation strategies for the species in this and similar landscapes.

Nonparametric Estimation of Conditional Quantile Regression with Mixed Discrete and Continuous Data

In this paper, we investigate the nonlinear quantile regression with mixed discrete and continuous regressors. A local linear smoothing technique with the mixed continuous and discrete kernel function is proposed to estimate the conditional quantile regression function. Under some mild conditions, the asymptotic distribution is established for the proposed nonparametric estimators, which can be seen as a generalisation of some existing theory which only handles the case of purely continuous regressors. We further study the choice of the tuning parameters in the local quantile estimation procedure, and suggest using the cross-validation approach to choose the optimal bandwidths. A simulation study is provided to examine the finite sample behavior of the proposed method, which is also compared with the naive local linear quantile estimation without smoothing the discrete regressors and the nonparametric inverse-CDF method proposed by Li, Lin and Racine (2013).

English

HIV treatment has been greatly impacted by transmitted resistance to antiretrovirals (ARV). Several studies have documented resistance in naive individuals and estimates of transmitted drug resistance mutations range from <5% to as high as 25%. Washington, D.C. has one of the highest human immunodeficiency virus (HIV) prevalence rates in the United States (3.2% in 2009), but local data regarding the frequency of major mutations and antiretroviral (ARV) resistance has been limited. Medical records of HIV positive, ARV-naive adults at two facilities in Washington, D.C., The George Washington University Medical Center and the Veterans Affairs Medical Center, were retrospectively analyzed in subjects who had genotypic resistance testing from 2007 to 2010. Of 407 ARV-naive patients, at least one transmitted drug resistance mutation was detected in 17% of our patients, with non-nucleoside reverse transcriptase (NNRTI) mutations observed in 15%. Among patients with at least one reverse transcriptase (RT) or major protease region (Pr) resistance mutation, 85% had resistance against a single ARV class. Dual and triple class resistance mutations were seen in 8 patients (2%) and 3 patients (0.7%), respectively. Most of the multiple class resistance was seen in 2010. A gradual increase in NNRTI resistance was noted during 2008 to 2010. Our prevalence of transmitted RT, major Pr mutations (17.4%) and ARV resistance (8.6%) were high but similar to rates reported by others within the United States. Given the high HIV prevalence in the District of Columbia, this has important implications for treatment of these ARV-naive patients. Key words: HIV, Washington D.C., naive to antiretrovirals, transmitted drug resistance, transmitted drug resistance mutations, antiretroviral mutations.

Decomposing Intergenerational Income Elasticity: The Gender-differentiated Contribution of Capital Transmission in Rural Philippines

Summary Using longitudinal data from rural Philippines, we decompose intergenerational income elasticity (IGE) into five distinct pathways: intergenerational transmissions of health, education, land, and spouse education capital, plus residual correlation in productivity. Intergenerational human capital transmissions from mothers are stronger than those from fathers; maternal education is the parental capital most strongly associated with IGE. While naive IGE estimates are indistinguishable for sons and daughters, the pathways that generate these results differ strikingly. For sons, IGE is entirely explained by parent-to-child capital transmission. By contrast, strong income correlation exists between daughters and parents even after controlling for parent and child capital endowments.

Smoothed estimator of quantile residual lifetime for right censored data

It is of great interest to estimate quantile residual lifetime in medical science and many other fields. In survival analysis, Kaplan-Meier (K-M) estimator has been widely used to estimate the survival distribution. However, it is well-known that the K-M estimator is not continuous, thus it can not always be used to calculate quantile residual lifetime. In this paper, the authors propose a kernel smoothing method to give an estimator of quantile residual lifetime. By using modern empirical process techniques, the consistency and the asymptotic normality of the proposed estimator are provided neatly. The authors also present the empirical small sample performances of the estimator. Deficiency is introduced to compare the performance of the proposed estimator with the naive unsmoothed estimator of the quantile residaul lifetime. Further simulation studies indicate that the proposed estimator performs very well.