Last modified: May 04, 2025

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Regression Analysis

Regression analysis and curve fitting are important tools in statistics, econometrics, engineering, and modern machine-learning pipelines. At their core they seek a deterministic (or probabilistic) mapping $\widehat f: \mathcal X \longrightarrow \mathcal Y$ that minimizes a suitably chosen loss function with respect to a sample of observations $\mathcal D = {(\mathbf x_1,y_1),\dots,(\mathbf x_N,y_N)}\subseteq \mathcal X\times\mathcal Y.$

A regression problem is typically posed under the additive error model

$$ y_i = f_*(\mathbf{x}_i) + \varepsilon_i, \qquad \mathbb{E}[\varepsilon_i \mid \mathbf{x}_i] = 0, \qquad \mathrm{Var}(\varepsilon_i) = \sigma^2. $$

where $f_*$ is an (unknown) deterministic function and $(\varepsilon_i)$ are random errors. The analyst’s objective is to construct an estimator $\widehat f$ (or equivalently to estimate a parameter vector $\widehat{\boldsymbol\theta}$ specifying $\widehat f$) such that some notion of risk—mean-squared error, negative log-likelihood, predictive log-loss, etc.—is minimized.

Curve Fitting

Curve fitting emphasizes the geometrical problem of approximating a cloud of points by a parametric curve or surface. The archetypal formulation is polynomial least-squares: given scalar inputs $x_i\in\mathbb R$, fit an $m$-degree polynomial

$P_m(x)=\sum_{k=0}^{m} a_k x^{k}\quad (\boldsymbol a\in\mathbb R^{m+1})$

by minimizing the sum-of-squares loss

$$ S(\mathbf{a}) = \sum_{i=1}^N \bigl(P_m(x_i) - y_i\bigr)^2. $$

In matrix form let $\mathbf V\in\mathbb R^{N\times(m+1)}$ be the Vandermonde matrix with $V_{ik}=x_i^{k}$ and $\mathbf a=(a_0,\dots,a_m)^\top$. The normal equations read $\mathbf V^{\top}\mathbf V\,\widehat{\mathbf a}=\mathbf V^{\top}\mathbf y.$

Provided $\mathbf V^{\top}\mathbf V$ is nonsingular (which fails if $m \ge N$ or data are collinear), the minimizer is uniquely given by $\widehat{\mathbf a}=(\mathbf V^{\top}\mathbf V)^{-1}\mathbf V^{\top}\mathbf y.$

Remark (Overfitting and Regularisation). High-degree polynomials can interpolate noisy data yet extrapolate disastrously. Ridge ($\ell_2$) or Lasso ($\ell_1$) penalties enforce smoothness or sparsity:

$$ S_\lambda(a) = \lVert V\,a - y\rVert_2^2 + \lambda\,\lVert a\rVert_q^q,\quad q\in{1,2} $$

Closed-form solutions exist for $q=2$; for $q=1$ one must resort to convex optimisation.

Other classical curve-fitting families include splines, B-splines, Bezier curves, wavelet bases, and kernel smoothers (e.g. Nadaraya–Watson). Each trades parametric flexibility against interpretability and computational cost.

Regression Analysis

In modern statistics, regression refers to modeling the conditional mean

$$ \mathbb{E}[\,y\mid x\,] = \mu(x;\,\beta), $$

where $\mu(\cdot;\beta)$ is a known function (link) indexed by parameters $\beta$. Given i.i.d. samples $(x_i,y_i)$, our goal is to estimate $\beta$.

Linear Model

If

$$ \mu(x;\beta) = x^\top \beta, $$

the model is linear in the parameters. Writing the data matrix $X$ and response vector $y$, the OLS estimator solves

$$ \hat\beta = \arg\min_{\beta}\,\|\,y - X\beta\|_2^2. $$

When $\mathrm{rank}(X)=p$, the closed-form solution is

$$ \hat\beta = (X^\top X)^{-1}X^\top y. $$

Gauss–Markov Theorem. If $\mathrm{Cov}(\varepsilon)=\sigma^2I$, then among all linear unbiased estimators $\tilde\beta = Cy$ with $CX=I$, OLS has the smallest variance:

$$ \mathrm{Var}(\tilde\beta) - \mathrm{Var}(\hat\beta) \succeq 0. $$

Generalized Linear Model (GLM)

For responses in the exponential family (e.g.\ Bernoulli, Poisson), we introduce a link $g$ so that

$$ g\bigl(\mu(x)\bigr) = x^\top \beta. $$

For instance, in logistic regression $g(\mu)=\log\bigl(\mu/(1-\mu)\bigr)$. Parameters are found by maximizing the likelihood

$$ \hat\beta = \arg\max_{\beta}\prod_{i=1}^{N} f\bigl(y_i;\,\mu(x_i;\beta)\bigr), $$

using Fisher scoring or Newton methods.

Nonlinear Least Squares (NLS)

When $\mu(x;\beta)$ is nonlinear in $\beta$ (e.g.\ Michaelis–Menten: $\mu(x;V,K)=Vx/(K+x)$), we minimize

$$ S(\beta) = \sum_{i=1}^N \bigl(y_i - \mu(x_i;\beta)\bigr)^2. $$

This loss is generally non-convex; standard solvers include Levenberg–Marquardt or trust-region algorithms.

Concepts in Regression

Types of Regression Methods

1. Ordinary Least Squares (OLS) – Closed-form, BLUE under Gauss–Markov conditions.
2. Ridge Regression – Penalised least-squares with $\lambda|\boldsymbol\beta|_2^2$; solution $\widehat{\boldsymbol\beta}=(\mathbf X^{\top}\mathbf X+\lambda\mathbf I)^{-1}\mathbf X^{\top}\mathbf y$.
3. Lasso & Elastic Net – $\ell_1$ and mixed $\ell_1+\ell_2$ penalties promoting sparsity; solved by coordinate descent or LARS.
4. Generalised Linear Models (GLM) – Logistic, probit, Poisson; estimated by iteratively re-weighted least squares.
5. Non-linear Regression (NLS) – Use gradient-based optimisers; asymptotic theory requires identifiability and regularity.
6. Robust Regression – M-estimators with Huber or Tukey bisquare $\rho$-functions; minimises $\sum_{i}\rho(r_i/\hat\sigma)$.
7. Quantile Regression – Minimises asymmetric absolute loss $\sum_{i}\rho_\tau(r_i)$ with $\rho_\tau(u)=u(\tau-\mathbb 1_{u<0})$.
8. Bayesian Regression – Places prior $p(\boldsymbol\beta)$, outputs posterior $p(\boldsymbol\beta\mid\mathbf y)\propto L(\boldsymbol\beta),p(\boldsymbol\beta)$; predictive distribution integrates over posterior.

Computational Note. High-dimensional ($p\gg N$) problems demand numerical linear-algebra tricks: Woodbury identity, iterative conjugate gradient, stochastic gradient descent (SGD), or variance-reduced methods (SVRG, SAGA).

Worked Examples

Example 1 – OLS in Matrix Form

We have $N=5$ observations ${(x_i,y_i)}$ and wish to fit

$$ y_i = \beta_0 + \beta_1 x_i + \varepsilon_i,\qquad \varepsilon_i\sim\text{mean }0. $$

We stack the data as

$$ X = \begin{bmatrix} 1 & 0.8\\ 1 & 1.2\\ 1 & 1.9\\ 1 & 2.4\\ 1 & 3.0 \end{bmatrix}, \qquad y = \begin{bmatrix} 1.2\\ 1.9\\ 3.1\\ 3.9\\ 5.1 \end{bmatrix}. $$

The OLS estimator is

$$ \hat\beta = (X^\top X)^{-1}\,X^\top y. $$

Compute

$$ X^\top X = \begin{bmatrix} 5 & 9.30\\ 9.30&20.45 \end{bmatrix}, \quad X^\top y = \begin{bmatrix} 15.20\\ 33.79 \end{bmatrix}. $$

Hence

$$ \hat\beta = \begin{pmatrix}\hat\beta_0\\hat\beta_1\end{pmatrix} \approx \begin{pmatrix}-0.236, 1.751\end{pmatrix}. $$

The fitted line is

$$ \hat y = -0.236 + 1.751\,x. $$

To assess fit, let $\bar y=15.20/5=3.04$. Then

$$ R^2 = 1 - \frac{\sum_i (y_i - \hat y_i)^2}{\sum_i (y_i - \bar y)^2} \approx 0.998. $$

Example 2 – Logistic Regression, MLE Derivatives

For binary data $y_i\in{0,1}$ the log-likelihood is

$$ \ell(\beta) = \sum_{i=1}^N \bigl[y_i\,x_i^\top \beta - \log\bigl(1 + e^{x_i^\top \beta}\bigr)\bigr]. $$

Gradient and Hessian:

$$ \nabla\ell(\beta) = X^\top (y - \pi), \quad \pi = (1 + e^{-X\beta})^{-1}, $$

$$ \nabla^2\ell(\beta) = -\,X^\top \mathrm{diag}\bigl(\pi \circ (1 - \pi)\bigr)\,X \preceq0. $$

Newton iteration: $\boldsymbol\beta^{(t+1)}=\boldsymbol\beta^{(t)}-(\nabla^2\ell)^{-1}\nabla\ell$.

Applications

• Finance & Econometrics – Capital asset pricing (CAPM), term-structure models, volatility forecasting (GARCH regression), default-probability prediction.
• Healthcare & Epidemiology – Survival analysis (Cox proportional hazards), dose-response curves, genome-wide association studies (GWAS) via penalised regression.
• Engineering – System identification, Kalman-filter regressions, fatigue-life modelling.
• Marketing & A/B Testing – Uplift modelling, mixed-effect regressions for hierarchical data.
• Machine Learning Pipelines – Feature engineering baseline, stacking/blending meta-learners, interpretability audits.

Limitations & Pitfalls

I. Model Misspecification:

When $f_*(\mathbf{x})$ lies outside the chosen hypothesis class, estimators remain biased even as $N \to \infty$.

II. Violation of IID:

Autocorrelated or clustered errors require GLS or “sandwich” covariance estimators.

III. Heteroscedasticity:

If $Var(\varepsilon_i \mid \mathbf{x}_i) = \sigma_i^2$, the usual OLS variance formula is invalid; use White’s (HC) estimators instead.

IV. Multicollinearity:

Near–linear dependence among columns of $X$ inflates $Var(\hat\beta_j)$; ridge regression can shrink the condition number.

V. High Use & Outliers:

Cook’s distance

$$D_i = \frac{r_i^2,h_{ii}}{p,\hat\sigma^2,(1 - h_{ii})^2}$$

identifies influential points; strong M–estimators mitigate their effect.

VI. Overfitting / High Variance:

Cross-validation, information criteria, or Bayesian model averaging help choose model complexity.

VII. External Validity:

Regression learns the conditional mean on $\mathcal{D}$; distribution shifts (covariate shift, concept drift) break prediction accuracy.

VIII. Causal Inference vs. Prediction:

Regression coefficients are not causal unless confounding is addressed (e.g.\ via instrumental variables, RCTs, or DAG-based adjustment).

Further Reading

1. Seber, G. A. F., & Lee, A. J. Linear Regression Analysis, 2e, Wiley (2003).
2. Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning, 2e, Springer (2009).
3. McCullagh, P., & Nelder, J. Generalized Linear Models, 2e, Chapman & Hall (1989).
4. Kennedy, P. A Guide to Econometrics, 7e, Wiley-Blackwell (2008).