Relevant bibliographies by topics / Estimating function

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Estimating function'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Estimating function"

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Liang, Y., A. Thavaneswaran, and B. Abraham. "Joint Estimation Using Quadratic Estimating Function." Journal of Probability and Statistics 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/372512.

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A class of martingale estimating functions is convenient and plays an important role for inference for nonlinear time series models. However, when the information about the first four conditional moments of the observed process becomes available, the quadratic estimating functions are more informative. In this paper, a general framework for joint estimation of conditional mean and variance parameters in time series models using quadratic estimating functions is developed. Superiority of the approach is demonstrated by comparing the information associated with the optimal quadratic estimating function with the information associated with other estimating functions. The method is used to study the optimal quadratic estimating functions of the parameters of autoregressive conditional duration (ACD) models, random coefficient autoregressive (RCA) models, doubly stochastic models and regression models with ARCH errors. Closed-form expressions for the information gain are also discussed in some detail.
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Thavaneswaran, Aerambamoorthy, Saumen Mandal, and Dharini Pathmanathan. "Estimation for Wrapped Zero Inflated Poisson and Wrapped Poisson Distributions." International Journal of Statistics and Probability 5, no. 3 (2016): 1. http://dx.doi.org/10.5539/ijsp.v5n3p1.

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There has been a growing interest in discrete circular models such as wrapped zero inflated Poisson and wrapped Poisson distributions and the trigonometric moments (see Brobbey et al., 2016 and Girija et al., 2014). Also, characteristic functions of stable processes have been used to study the estimation of the model parameters using estimating function approach (see Thavaneswaran et al., 2013). One difficulty in estimating the circular mean and the resultant mean length parameter of wrapped Poisson (WP) or wrapped zero inflated Poisson (WZIP) is that neither the likelihood of WP/WZIP random variable nor the score function is available in closed form, which leads one to use either trigonometric method of moment estimation (TMME) or an estimating function approach. In this paper, we study the estimation of WZIP distribution and WP distribution using estimating functions and obtain the closed form expression of the information matrix. We also derive the asymptotic distribution of the tangent of the mean direction for both the WZIP and WP distributions.
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Miura, Keiji, Masato Okada, and Shun-ichi Amari. "Estimating Spiking Irregularities Under Changing Environments." Neural Computation 18, no. 10 (2006): 2359–86. http://dx.doi.org/10.1162/neco.2006.18.10.2359.

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We considered a gammadistribution of interspike intervals as a statistical model for neuronal spike generation. A gamma distribution is a natural extension of the Poisson process taking the effect of a refractory period into account. The model is specified by two parameters: a time-dependent firing rate and a shape parameter that characterizes spiking irregularities of individual neurons. Because the environment changes over time, observed data are generated from a model with a time-dependent firing rate, which is an unknown function. A statistical model with an unknown function is called a semiparametric model and is generally very difficult to solve. We used a novel method of estimating functions in information geometry to estimate the shape parameter without estimating the unknown function. We obtained an optimal estimating function analytically for the shape parameter independent of the functional form of the firing rate. This estimation is efficient without Fisher information loss and better than maximum likelihood estimation. We suggest a measure of spiking irregularity based on the estimating function, which may be useful for characterizing individual neurons in changing environments.
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Zhang, Yunyi, Jiazheng Liu, Zexin Pan, and Dimitris N. Politis. "Estimating transformation function." Electronic Journal of Statistics 13, no. 2 (2019): 3095–119. http://dx.doi.org/10.1214/19-ejs1603.

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Mortici, Cristinel. "Estimating gamma function by digamma function." Mathematical and Computer Modelling 52, no. 5-6 (2010): 942–46. http://dx.doi.org/10.1016/j.mcm.2010.05.030.

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Tong, Tiejun, Yanyuan Ma, and Yuedong Wang. "Optimal variance estimation without estimating the mean function." Bernoulli 19, no. 5A (2013): 1839–54. http://dx.doi.org/10.3150/12-bej432.

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Hu, Feifang, and John D. Kalbfleisch. "The estimating function bootstrap." Canadian Journal of Statistics 28, no. 3 (2000): 449–81. http://dx.doi.org/10.2307/3315958.

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Kundu, Piyali, Somesh Kumar, and Kashinath Chatterjee. "Estimating the Reliability Function." Calcutta Statistical Association Bulletin 67, no. 3-4 (2015): 143–61. http://dx.doi.org/10.1177/0008068320150304.

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Roy, R., P. Souchoroukov, and T. Griggs. "Function-based cost estimating." International Journal of Production Research 46, no. 10 (2008): 2621–50. http://dx.doi.org/10.1080/00207540601094440.

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Peszek, Iza, and Andrew L. Rukhin. "Estimating lognormal hazard function." Journal of Statistical Planning and Inference 41, no. 3 (1994): 281–90. http://dx.doi.org/10.1016/0378-3758(94)90024-8.

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Dissertations / Theses on the topic "Estimating function"

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Liang, Longjuan. "A semi-parametric approach to estimating item response functions." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180453363.

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Rahikainen, I. (Ilkka). "Direct methodology for estimating the risk neutral probability density function." Master's thesis, University of Oulu, 2014. http://urn.fi/URN:NBN:fi:oulu-201404241289.

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The target of the study is to find out if the direct methodology could provide same information about the parameters of the risk neutral probability density function (RND) than the reference RND methodologies. The direct methodology is based on for defining the parameters of the RND from underlying asset by using futures contracts and only few at-the-money (ATM) and/or close at-the-money (ATM) options on asset. Of course for enabling the analysis of the feasibility of the direct methodology the reference RNDs must be estimated from the option data. Finally the results of estimating the parameters by the direct methodology are compared to the results of estimating the parameters by the selected reference methodologies for understanding if the direct methodology can be used for understanding the key parameters of the RND. The study is based on S&P 500 index option data from year 2008 for estimating the reference RNDs and for defining the reference moments from the reference RNDs. The S&P 500 futures contract data is necessary for finding the expectation value estimation for the direct methodology. Only few ATM and/or close ATM options from the S&P 500 index option data are necessary for getting the standard deviation estimation for the direct methodology. Both parametric and non-parametric methods were implemented for defining reference RNDs. The reference RND estimation results are presented so that the reference RND estimation methodologies can be compared to each other. The moments of the reference RNDs were calculated from the RND estimation results so that the moments of the direct methodology can be compared to the moments of the reference methodologies. The futures contracts are used in the direct methodology for getting the expectation value estimation of the RND. Only few ATM and/or close ATM options are used in the direct methodology for getting the standard deviation estimation of the RND. The implied volatility is calculated from option prices using ATM and/or close ATM options only. Based on implied volatility the standard deviation can be calculated directly using time scaling equations. Skewness and kurtosis can be calculated from the estimated expectation value and the estimated standard deviation by using the assumption of the lognormal distribution. Based on the results the direct methodology is acceptable for getting the expectation value estimation using the futures contract value directly instead of the expectation value, which is calculated from the RND of full option data, if and only if the time to maturity is relative short. The standard deviation estimation can be calculated from few ATM and/or at close ATM options instead of calculating the RND from full option data only if the time to maturity is relative short. Skewness and kurtosis were calculated from the expectation value estimation and the standard deviation estimation by using the assumption of the lognormal distribution. Skewness and kurtosis could not be estimated by using the assumption of the lognormal distribution because the lognormal distribution is not correct generic assumption for the RND distributions.
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Farquharson, Maree Louise. "Estimating the parameters of polynomial phase signals." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16312/1/Maree_Farquharson_Thesis.pdf.

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Nonstationary signals are common in many environments such as radar, sonar, bioengineering and power systems. The nonstationary nature of the signals found in these environments means that classicalspectralanalysis techniques are notappropriate for estimating the parameters of these signals. Therefore it is important to develop techniques that can accommodate nonstationary signals. This thesis seeks to achieve this by firstly, modelling each component of the signal as having a polynomial phase and by secondly, developing techniques for estimating the parameters of these components. Several approaches can be used for estimating the parameters of polynomial phase signals, eachwithvarying degrees ofsuccess.Criteria to consider in potential estimation algorithms are (i) the signal-to-noise (SNR) ratio threshold of the algorithm, (ii) the amount of computation required for running the algorithm, and (iii) the closeness of the resulting estimates' mean-square errors to the minimum theoretical bound. These criteria will be used to compare the new techniques developed in this thesis with existing techniques. The literature on polynomial phase signal estimation highlights the recurring trade-off between the accuracy of the estimates and the amount of computation required. For example, the Maximum Likelihood (ML) method provides near-optimal estimates above threshold, but also incurs a heavy computational cost for higher order phase signals. On the other hand, multi-linear techniques such as the high-order ambiguity function (HAF) method require little computation, but have a significantly higher SNR threshold than the ML method. Of the existing techniques, the cubic phase (CP) function method is a promising technique because it provides an attractive SNR threshold and computational complexity trade-off. For this reason, the analysis techniques developed in this thesis will be derived from the CP function. A limitation of the CP function is its inability to accurately process phase orders greater than three. Therefore, the first novel contribution to this thesis develops a broadened class of discrete-time higher order phase (HP)functions to address this limitation.This broadened class is achieved by providing a multi-linear extension of the CP function. Monte Carlo simulations are performed to demonstrate the statistical advantage of the HP functions compared to the HAFs. A first order statistical analysis of the HP functions is presented. This analysis verifies the simulation results. The next novel contribution is a technique called the lower SNR cubic phase function (LCPF)method. It is an extension of the CP function, with the extension enabling performance at lower signal-to-noise ratios (SNRs). The improvement of the SNR threshold's performance is achieved by coherently integrating the CP function over a compact interval in the two-dimensional CP function space. The computation of the new algorithm is quite moderate, especially when compared to the ML method. Above threshold, the LCPF method's parameter estimates are asymptotically efficient. Monte Carlo simulation results are presented and a threshold analysis of the algorithm closely predicts the thresholds observed in these results. The next original contribution to this research involves extending the LCPF method so that it is able to process multicomponent cubic phase signals and higher order phase signals. The LCPF method is extended to higher orders by applying a windowing technique as opposed to adjusting the order of the kernel as implemented in the HP function method. To demonstrate the extension of the LCPF method for processing higher order phase signals and multicomponent cubic phase signals, some Monte Carlo simulations are presented. Finally, these estimation techniques are applied to real-worldscenarios in the fields of Power Systems Analysis, Neuroethology and Speech Analysis.
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Farquharson, Maree Louise. "Estimating the parameters of polynomial phase signals." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16312/.

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Nonstationary signals are common in many environments such as radar, sonar, bioengineering and power systems. The nonstationary nature of the signals found in these environments means that classicalspectralanalysis techniques are notappropriate for estimating the parameters of these signals. Therefore it is important to develop techniques that can accommodate nonstationary signals. This thesis seeks to achieve this by firstly, modelling each component of the signal as having a polynomial phase and by secondly, developing techniques for estimating the parameters of these components. Several approaches can be used for estimating the parameters of polynomial phase signals, eachwithvarying degrees ofsuccess.Criteria to consider in potential estimation algorithms are (i) the signal-to-noise (SNR) ratio threshold of the algorithm, (ii) the amount of computation required for running the algorithm, and (iii) the closeness of the resulting estimates' mean-square errors to the minimum theoretical bound. These criteria will be used to compare the new techniques developed in this thesis with existing techniques. The literature on polynomial phase signal estimation highlights the recurring trade-off between the accuracy of the estimates and the amount of computation required. For example, the Maximum Likelihood (ML) method provides near-optimal estimates above threshold, but also incurs a heavy computational cost for higher order phase signals. On the other hand, multi-linear techniques such as the high-order ambiguity function (HAF) method require little computation, but have a significantly higher SNR threshold than the ML method. Of the existing techniques, the cubic phase (CP) function method is a promising technique because it provides an attractive SNR threshold and computational complexity trade-off. For this reason, the analysis techniques developed in this thesis will be derived from the CP function. A limitation of the CP function is its inability to accurately process phase orders greater than three. Therefore, the first novel contribution to this thesis develops a broadened class of discrete-time higher order phase (HP)functions to address this limitation.This broadened class is achieved by providing a multi-linear extension of the CP function. Monte Carlo simulations are performed to demonstrate the statistical advantage of the HP functions compared to the HAFs. A first order statistical analysis of the HP functions is presented. This analysis verifies the simulation results. The next novel contribution is a technique called the lower SNR cubic phase function (LCPF)method. It is an extension of the CP function, with the extension enabling performance at lower signal-to-noise ratios (SNRs). The improvement of the SNR threshold's performance is achieved by coherently integrating the CP function over a compact interval in the two-dimensional CP function space. The computation of the new algorithm is quite moderate, especially when compared to the ML method. Above threshold, the LCPF method's parameter estimates are asymptotically efficient. Monte Carlo simulation results are presented and a threshold analysis of the algorithm closely predicts the thresholds observed in these results. The next original contribution to this research involves extending the LCPF method so that it is able to process multicomponent cubic phase signals and higher order phase signals. The LCPF method is extended to higher orders by applying a windowing technique as opposed to adjusting the order of the kernel as implemented in the HP function method. To demonstrate the extension of the LCPF method for processing higher order phase signals and multicomponent cubic phase signals, some Monte Carlo simulations are presented. Finally, these estimation techniques are applied to real-worldscenarios in the fields of Power Systems Analysis, Neuroethology and Speech Analysis.
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SALGADO, MARIA JOSE SEUANEZ. "MONETARY POLICY DURING THE REAL PLAN: ESTIMATING THE CENTRAL BANKS REACTION FUNCTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2001. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14073@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Esta dissertação visa estudar a função de reação do Banco Central do Brasil durante o Plano Real. Argumenta-se que a taxa de juros nominal foi o instrumento mais importante de política monetária, sendo ajustado como resposta a variações na taxa de inflação, hiato do produto, reservas internacionais e ao seu próprio defasado. Estima-se então um modelo linear para a taxa de juros nominal. Em seguida, um Modelo como Limiar (modelo TAR) é usado para explicar uma mudança de regime na taxa de juros. Usando um indicador de crises cambiais, que é escolhido endogenamente, o modelo tenta explicar a diferença na dinâmica da taxa de juros durante e fora das crises. O modelo linear e o não-linear são então comparados e conclui-se que a última abordagem é a mais adequada para estudar a função de reação do Banco Central do Brasil.<br>This dissertation studies the Central Bank of Brazil`s reaction function during the Real Plan. It is argued that the nominal interest rate was the most important monetary policy instrument, being adjusted to changes in the rate of inflation, output gap, international reserves and its own lagged value. First, a linear model is estimated for the nominal interest rate. Second, a Threshold Autoregressive model with exogenous variables is used to explain a change in regime in interest rates. By using an indicator of currency crises, which is chosen endogenously, the model tries to explain the difference in dynamic of nominal interest rates during and out of a currency crises. The paper then compares the linear and non-linear models and shows that the latter performs considerably better than the former.
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Alnaji, Lulah A. "Generalized Estimating Equations for Mixed Models." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530292694012892.

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Glórias, Ludgero Miguel Carraça. "Estimating a knowledge production function and knowledge spillovers : a new two-step estimation procedure of a Spatial Autoregressive Poisson Model." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20711.

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Mestrado em Econometria Aplicada e Previsão<br>Vários estudos econométricos procuram explicar os determinantes da criação de conhecimento usando como variável dependente o número de patenteamentos numa região. Alguns destes procuram captar os efeitos de Knowledge Spillovers através de modelos lineares que incorporam dependência espacial. No entanto, nenhum estudo foi encontrado que captasse este efeito, tendo em atenção a natureza discreta da variável dependente. Este trabalho pretende preencher essa lacuna propondo um novo estimador de máxima verosimilhança a dois passos para um modelo Poisson Autorregressivo Espacial. As propriedades do estimador são avaliadas num conjunto de simulações de Monte Carlo. Os resultados sugerem que este estimador tem menor Bias e menor RMSE, na generalidade, que outros estimadores propostos, sendo que apenas mostra piores resultados quando a dependência espacial é próxima da unidade. Um exemplo empírico, empregando o novo estimador e um conjunto de estimadores alternativos, é realizado, sendo que a criação de conhecimento em 234 NUTS II de 24 países europeus é analisada. Os resultados evidenciam que existe uma forte dependência espacial na criação de inovação entre as regiões. Conclui-se também que o ambiente socioeconómico é essencial para o processo de formação de conhecimento e que contrariamente às instituições públicas, as empresas privadas são eficientes na produção de inovação. É de realçar, que regiões com menor capacidade em transformar despesas R&D em patenteamentos apresentam maior capacidade de absorção e segregação de conhecimento, evidenciando que regiões vizinhas menos eficientes na produção de conhecimento tendem a criar relações fortalecidas na partilha de conhecimento.<br>Several econometric studies seek to explain the determinants of knowledge production using as dependent variable the number of patents in a region. Some of these capture the effects of knowledge spillovers through linear models with spatial autorregressive term. However, no study has been found that estimates such effect while also considering the discrete nature of the dependent variable: a count variable. This essay aims to fill this gap by proposing a new Two-step Maximum Likelihood estimator for a Spatial Autorregressive Poisson model. The properties of this estimator are evaluated in a set of Monte Carlo Experiments. The simulation results suggest that this estimator presents lower Bias and lower RMSE than the alternative estimators proposed, only showing worse results when the spatial dependence is close to the unit. An empirical example, using the new estimator and a set of alternative estimators, is executed, where the creation of knowledge in 234 NUTS II from 24 European countries is analyzed. The results show that there is a strong spatial dependence on the creation of innovation. It is also concluded that the socio-economic environment is essential for the knowledge formation and, unlike public R&D institutions, private companies are efficient in producing innovation. It should be noted that regions with less capacity to transform R&D expenses into new patents, have greater capacity for absorption and segregation of knowledge, which shows that neighboring regions less efficient in the production of knowledge tend to create strong relations with each other taking advantage of the knowledge sharing process.<br>info:eu-repo/semantics/publishedVersion
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Cheng, Gang. "The nonparametric least-squares method for estimating monotone functions with interval-censored observations." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2839.

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Monotone function, such as growth function and cumulative distribution function, is often a study of interest in statistical literature. In this dissertation, we propose a nonparametric least-squares method for estimating monotone functions induced from stochastic processes in which the starting time of the process is subject to interval censoring. We apply this method to estimate the mean function of tumor growth with the data from either animal experiments or tumor screening programs to investigate tumor progression. In this type of application, the tumor onset time is observed within an interval. The proposed method can also be used to estimate the cumulative distribution function of the elapsed time between two related events in human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) studies, such as HIV transmission time between two partners and AIDS incubation time from HIV infection to AIDS onset. In these applications, both the initial event and the subsequent event are only known to occur within some intervals. Such data are called doubly interval-censored data. The common property of these stochastic processes is that the starting time of the process is subject to interval censoring. A unified two-step nonparametric estimation procedure is proposed for these problems. In the first step of this method, the nonparametric maximum likelihood estimate (NPMLE) of the cumulative distribution function for the starting time of the stochastic process is estimated with the framework of interval-censored data. In the second step, a specially designed least-squares objective function is constructed with the above NPMLE plugged in and the nonparametric least-squares estimate (NPLSE) of the mean function of tumor growth or the cumulative distribution function of the elapsed time is obtained by minimizing the aforementioned objective function. The theory of modern empirical process is applied to prove the consistency of the proposed NPLSE. Simulation studies are extensively carried out to provide numerical evidence for the validity of the NPLSE. The proposed estimation method is applied to two real scientific applications. For the first application, California Partners' Study, we estimate the distribution function of HIV transmission time between two partners. In the second application, the NPLSEs of the mean functions of tumor growth are estimated for tumors with different stages at diagnosis based on the data from a cancer surveillance program, the SEER program. An ad-hoc nonparametric statistic is designed to test the difference between two monotone functions under this context. In this dissertation, we also propose a numerical algorithm, the projected Newton-Raphson algorithm, to compute the non– and semi-parametric estimate for the M-estimation problems subject to linear equality or inequality constraints. By combining the Newton-Raphson algorithm and the dual method for strictly convex quadratic programming, the projected Newton-Raphson algorithm shows the desired convergence rate. Compared to the well-known iterative convex minorant algorithm, the projected Newton-Raphson algorithm achieves much quicker convergence when computing the non- and semi-parametric maximum likelihood estimate of panel count data.
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Lim, L. L.-Y. "Statistical methods for the assessment of lung function : Estimating the distribution of ventilation-perfusion ratio from inert gas experiments." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383447.

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Gavin, Victor S. "Evaluation of cost estimating methods for military software application in a COTS environment." Master's thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-02232010-020031/.

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Books on the topic "Estimating function"

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Harrison, R. S. Managing the estimating function. Chartered Institute of Building, 1987.

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Langmaid, John. Estimating: Getting value from function. BSRIA, 2003.

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Drummond, Ian. Estimating with Mk II function point analysis. HMSO, 1992.

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Wildman, Valerie Jean. Estimating effective area surveyed with the cumulative distribution function. Dept. of Statistics, Oregon State University, 1985.

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Software sizing and estimating: Mk II FPA (function point analysis). Wiley, 1991.

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Canright, David. Estimating the spatial extent of attractors of iterated function systems. Naval Postgraduate School, 1993.

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Cuenco, Michael L. Framework for estimating salmon survival as a function of habitat condition. Columbia River Inter-Tribal Fish Commission, 1996.

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Treble, Stephen. Sizing and estimating software in practice: Making MKII function points work. McGraw-Hill, 1995.

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1962-, Douglas Neil, ed. Sizing and estimating software in practice: Making MKII function points work. McGraw-Hill, 1995.

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Attfield, C. L. F. Estimating the UK demand for money function: A test of two approaches. University of Bristol, Department of Economics, 1995.

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Book chapters on the topic "Estimating function"

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Qin, Jing. "Optimal Estimating Function Theory." In Biased Sampling, Over-identified Parameter Problems and Beyond. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4856-2_5.

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Date, Shailesh V. "Estimating Protein Function Using Protein-Protein Relationships." In Gene Function Analysis. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-547-3_7.

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Śliwiński, Przemysław. "Wavelet Network Estimating Regression Function." In Neural Networks and Soft Computing. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_112.

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Csörgő, Miklós, Lajos Horváth, and Paul Deheuvels. "Estimating the Quantile-Density Function." In Nonparametric Functional Estimation and Related Topics. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3222-0_16.

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Wang, Guoli, and Michael F. Ochs. "Estimating Gene Function With Least Squares Nonnegative Matrix Factorization." In Gene Function Analysis. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-547-3_3.

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Hu, Feifang. "Bootstrap, Markov Chain and Estimating Function." In Springer Handbook of Engineering Statistics. Springer London, 2006. http://dx.doi.org/10.1007/978-1-84628-288-1_37.

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Pal, Jayanta Kumar, Michael Woodroofe, and Mary Meyer. "Estimating a Polya frequency function$_2$." In Complex Datasets and Inverse Problems. Institute of Mathematical Statistics, 2007. http://dx.doi.org/10.1214/074921707000000184.

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Kang, Min-Jae, Chang-Jin Boo, Ho-Chan Kim, and Jacek M. Zurada. "Estimating Soil Parameters Using the Kernel Function." In Computational Science and Its Applications – ICCSA 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12165-4_9.

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Fischer, Bernd. "Estimating the Spectrum and the Distribution function." In Polynomial Based Iteration Methods for Symmetric Linear Systems. Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-663-11108-5_5.

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Ailon, Nir, Bernard Chazelle, Seshadhri Comandur, and Ding Liu. "Estimating the Distance to a Monotone Function." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27821-4_21.

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Conference papers on the topic "Estimating function"

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Jindal, Anshul, Mohak Chadha, Shajulin Benedict, and Michael Gerndt. "Estimating the capacities of function-as-a-service functions." In UCC '21: 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing. ACM, 2021. http://dx.doi.org/10.1145/3492323.3495628.

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Jakobsson, Andreas, Stephen R. Alty, and Jacob Benesty. "Estimating the Two-Dimensional Coherence Function." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366849.

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Harrison, Matthew, and Ioannis Kontoyiannis. "On Estimating the Rate-Distortion Function." In 2006 IEEE International Symposium on Information Theory. IEEE, 2006. http://dx.doi.org/10.1109/isit.2006.261847.

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Zeman, Jan. "Estimating of Bellman function via suboptimal strategies." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5641952.

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Kang, Byeongdo, and Jongseok Lee. "Estimating Procedure for Function Point Analysis in Korea." In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2018. http://dx.doi.org/10.1109/snpd.2018.8441088.

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Colson, T., B. Boterhoven, D. Castillo, and M. Keep. "Estimating the Coulomb Failure Function Using Seismic Velocities." In Fourth International Conference on Fault and Top Seals. EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201414069.

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Sikka, Geeta, Arvinder Kaur, and Moin Uddin. "Estimating function points: Using machine learning and regression models." In 2010 2nd International Conference on Education Technology and Computer (ICETC). IEEE, 2010. http://dx.doi.org/10.1109/icetc.2010.5529600.

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Shen, Jin, Gang Zheng, Mengchao Li, and Guoqiang Sun. "Improving methods of estimating autocorrelation function in PCS technique." In Photonics Asia 2002, edited by Zhicheng Weng, Jose M. Sasian, and Yongtian Wang. SPIE, 2002. http://dx.doi.org/10.1117/12.471673.

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Chelli, Ali, and Matthias Patzold. "An improved method for estimating the frequency correlation function." In 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2012. http://dx.doi.org/10.1109/wcnc.2012.6213930.

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Penmetsa, Ravi, and Ramana Grandhi. "Estimating Membership Function of Implicit Response Using Surrogate Models." In 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-1234.

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Reports on the topic "Estimating function"

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Hall, Peter, and R. J. Carroll. Variance Function Estimation in Regression: The Effect of Estimating the Mean. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198228.

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Al Gahtani, Goblan, Carlo Andrea Bollino, Simona Bigerna, and Axel Pierru. Estimating the Household Consumption Function in Saudi Arabia. King Abdullah Petroleum Studies and Research Center, 2019. http://dx.doi.org/10.30573/ks--2019-dp50.

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Canright, D. Estimating the Spatial Extent of Attractors of Iterated Function System. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada265856.

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Ito, Takatoshi, and Tomoyoshi Yabu. Japanese Foreign Exchange Interventions, 1971-2018: Estimating a Reaction Function Using the Best Proxy. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w26644.

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Attanasio, Orazio, Sarah Cattan, Emla Fitzsimons, Costas Meghir, and Marta Rubio-Codina. Estimating the Production Function for Human Capital: Results from a Randomized Control Trial in Colombia. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w20965.

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Attanasio, Orazio, Marta Rubio Codina, Sarah Cattan, Emla Fitzsimons, and Costas Meghir. Estimating the production function for human capital: results from a randomized controlled trial in Colombia. IFS, 2015. http://dx.doi.org/10.1920/wp.ifs.2015.1506.

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Rubio Codina, Marta, Orazio Attanasio, Sarah Cattan, Emla Fitzsimons, and Costas Meghir. Estimating the production function for human capital: results from a randomized controlled trial in Colombia. The IFS, 2017. http://dx.doi.org/10.1920/wp.ifs.2017.1706.

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Rubio Codina, Marta, Costas Meghir, Emla Fitzsimons, Sarah Cattan, and Orazio Attanasio. Estimating the production function for human capital: results from a randomized controlled trial in Colombia. The IFS, 2020. http://dx.doi.org/10.1920/wp.ifs.2020.320.

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Read, Matthew. Estimating the Effects of Monetary Policy in Australia Using Sign-restricted Structural Vector Autoregressions. Reserve Bank of Australia, 2023. http://dx.doi.org/10.47688/rdp2022-09.

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Existing estimates of the macroeconomic effects of Australian monetary policy tend to be based on strong, potentially contentious, assumptions. I estimate these effects under weaker assumptions. Specifically, I estimate a structural vector autoregression identified using a variety of sign restrictions, including restrictions on impulse responses to a monetary policy shock, the monetary policy reaction function, and the relationship between the monetary policy shock and a proxy for this shock. I use an approach to Bayesian inference that accounts for the problem of posterior sensitivity to the choice of prior that arises in this setting, which turns out to be important. Some sets of identifying restrictions are not particularly informative about the effects of monetary policy. However, combining the restrictions allows us to draw some useful inferences. There is robust evidence that an increase in the cash rate lowers output and consumer prices at horizons beyond a year or so. The results are consistent with the macroeconomic effects of a 100 basis point increase in the cash rate lying towards the upper end of the range of existing estimates.
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Kott, Phillip S. The Role of Weights in Regression Modeling and Imputation. RTI Press, 2022. http://dx.doi.org/10.3768/rtipress.2022.mr.0047.2203.

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When fitting observations from a complex survey, the standard regression model assumes that the expected value of the difference between the dependent variable and its model-based prediction is zero, regardless of the values of the explanatory variables. A rarely failing extended regression model assumes only that the model error is uncorrelated with the model’s explanatory variables. When the standard model holds, it is possible to create alternative analysis weights that retain the consistency of the model-parameter estimates while increasing their efficiency by scaling the inverse-probability weights by an appropriately chosen function of the explanatory variables. When a regression model is used to impute for missing item values in a complex survey and when item missingness is a function of the explanatory variables of the regression model and not the item value itself, near unbiasedness of an estimated item mean requires that either the standard regression model for the item in the population holds or the analysis weights incorporate a correctly specified and consistently estimated probability of item response. By estimating the parameters of the probability of item response with a calibration equation, one can sometimes account for item missingness that is (partially) a function of the item value itself.
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